Studio: forward standard OpenAI tools / tool_choice on /v1/responses (Codex compat) (#5122)

* Studio: forward standard OpenAI tools / tool_choice on /v1/responses

Mirrors the /v1/chat/completions client-side tool pass-through from #5099
so clients (OpenAI Codex CLI, OpenAI Python SDK, ...) that target the
Responses API receive structured function_call output items instead of
plain text with tool-call tokens leaking into content.

- ResponsesRequest: type tools/tool_choice properly, add parallel_tool_calls;
  accept function_call and function_call_output input items for multi-turn
- Translate flat Responses tool / tool_choice shape to the nested Chat
  Completions shape before forwarding to llama-server
- _normalise_responses_input: map function_call_output -> role="tool",
  function_call -> assistant tool_calls (preserving call_id)
- Non-streaming: map returned tool_calls -> top-level function_call
  output items keyed by call_id
- Streaming: emit response.output_item.added (function_call),
  response.function_call_arguments.delta/.done, and response.output_item.done
  per tool call while keeping the text message at output_index 0
- Pytest coverage: tools/tool_choice translation, multi-turn input mapping,
  non-streaming tool_calls mapping, response round-trip

* Studio: merge system messages and close inner stream on /v1/responses

Fixes two issues surfacing when OpenAI Codex CLI drives /v1/responses
against a GGUF with a strict chat template (gpt-oss harmony, Qwen3, ...).

1. "System message must be at the beginning" upstream errors
   Codex sends `instructions` AND a `role:"developer"` message in `input`,
   producing two separate system-role messages. Strict templates raise
   when a second system message exists or when one appears after a user
   turn. _normalise_responses_input now hoists all instructions / system /
   developer content into a single merged system message at the top of
   the Chat Completions message list.

2. "async generator ignored GeneratorExit" / "Attempted to exit cancel
   scope in a different task"
   _responses_stream consumed the inner chat-completions body_iterator
   without an explicit aclose() in a finally block. On client disconnect
   (Codex frequently cancels mid-stream), Python 3.13 finalized the inner
   async generator on a different task, tripping anyio's cancel-scope
   check. Mirrored the same try/finally + aclose pattern used by the
   /v1/messages, /v1/chat/completions, and /v1/completions passthroughs.

Tests: hoisting of instructions + developer, developer mid-conversation,
multiple system messages in input, no-system passthrough.

* Studio: accept Codex multi-turn shapes and fix cross-task stream close on /v1/responses

Two issues observed driving /v1/responses from OpenAI Codex CLI against a
GGUF backend.

1. 422 on every turn after the first
   Codex replays prior assistant turns with
   `content:[{"type":"output_text","text":...,"annotations":[],"logprobs":[]}]`
   and carries forward `reasoning` items (o-series / gpt-5) between turns.
   Our `ResponsesContentPart` union only accepted input_text / input_image,
   and `ResponsesInputItem` only message / function_call / function_call_output,
   so Pydantic failed the whole list and FastAPI returned
   `"Input should be a valid string"` against the `str` branch of the
   outer union.

   - Add `ResponsesOutputTextPart` for assistant-replay content.
   - Add `ResponsesUnknownContentPart` and `ResponsesUnknownInputItem`
     as permissive catch-alls (drop during normalisation).
   - Wire an explicit `Discriminator` so dispatch is deterministic and
     the fallthrough reaches the catch-all instead of misreporting via
     the outer `Union[str, list[...]]`.
   - `_normalise_responses_input` now accepts output_text parts, flattens
     single-part assistant text to a plain string (keeps legacy chat
     templates happy), and silently drops reasoning / unknown items.

2. "async generator ignored GeneratorExit" / cross-task cancel scope
   `_responses_stream` awaited `openai_chat_completions` in the parent
   route-handler task, which opens the httpx client for the inner
   passthrough on *that* task. The outer `StreamingResponse` then iterates
   in a child task, so the asyncgen GC finalises the inner httpcore byte
   stream on the child task, tripping anyio's "Attempted to exit cancel
   scope in a different task". Move the `await` inside `event_generator`
   so the httpx lifecycle stays within the single streaming child task,
   and surface any HTTPException as a `response.failed` SSE frame.

Tests: assistant output_text replay, reasoning-item tolerance, unknown
content-part tolerance, end-to-end Codex-shape payload (developer + user +
reasoning + function_call + function_call_output + assistant output_text +
user), and single-part assistant flattening to plain string.

* Studio: call llama-server directly from streaming /v1/responses

The previous fix (running the inner await inside event_generator) was not
enough. Wrapping the existing `openai_chat_completions` pass-through still
stacks two async generators: when the outer generator is closed, the
innermost `HTTP11ConnectionByteStream.__aiter__` in httpcore doesn't
receive GeneratorExit before Python's asyncgen GC finalises it in a
sibling task, tripping "Attempted to exit cancel scope in a different
task" and "async generator ignored GeneratorExit" — the same Python 3.13
+ httpcore 1.0.x interaction already seen in PRs #4956, #4981, #5099.

Cure both pass-throughs had: a single same-task httpx lifecycle with
explicit `aiter_lines().aclose()` BEFORE `resp.aclose()` / `client.aclose()`
in the generator's finally block.

Apply it at the Responses layer by dropping the wrapper entirely for GGUF:
open httpx, consume `resp.aiter_lines()`, parse `chat.completion.chunk`,
emit Responses SSE events, close everything in finally — all in the
single StreamingResponse child task. Non-GGUF streaming is rejected with
a 400 (wrapping the transformers backend would re-introduce the
double-layer pattern and isn't a Codex-compatible path today anyway).

Also surfaces upstream httpx.RequestError / non-200 as a
`response.failed` SSE frame rather than a dropped stream now that the
request is dispatched after SSE headers have gone out.

* Studio: silence benign httpcore asyncgen GC warnings on Python 3.13

The streaming pass-throughs (/v1/chat/completions, /v1/messages,
/v1/responses, /v1/completions) all use the proven #4981 / #5099 pattern
— single-task httpx lifecycle with explicit aiter_lines().aclose() ahead
of resp.aclose() / client.aclose() in the generator's finally block.
That handles our own iterators correctly.

The residual noise ("async generator ignored GeneratorExit" /
"Attempted to exit cancel scope in a different task") comes from an
innermost HTTP11ConnectionByteStream.__aiter__ that httpcore creates
internally inside its pool. We hold no reference to it, so we cannot
aclose it ourselves. Python 3.13's asyncgen GC hook finalises it on the
finaliser task, its aclose path enters an anyio CancelScope shield, and
Python flags the cross-task exit. The response has already been
delivered with a 200 by then — it is purely log noise, not a functional
failure. Same interaction seen in modelcontextprotocol/python-sdk #831,
agno #3556, chainlit #2361, langchain-mcp-adapters #254.

Install a targeted sys.unraisablehook that swallows this specific tuple
— RuntimeError mentioning "cancel scope" or "GeneratorExit" plus an
object repr referencing HTTP11ConnectionByteStream — and defers to the
default hook for every other unraisable. Idempotent; guarded by a
sentinel attribute so repeated imports don't stack filters.

.gitattributes

@@ -0,0 +1,2 @@
+# Normalize Python files to LF line endings
+*.py text eol=lf

.github/CODEOWNERS

@@ -0,0 +1,55 @@
+# Inspired from https://github.com/vllm-project/vllm/blob/main/.github/CODEOWNERS
+
+/unsloth/models/loader.py @danielhanchen @mmathew23
+/unsloth/models/llama.py @Datta0 @danielhanchen @mmathew23
+/unsloth/models/rl.py @Datta0 @pluesclues @danielhanchen
+/unsloth/models/rl_replacements.py @Datta0 @pluesclues @danielhanchen
+/unsloth/trainer.py @danielhanchen
+/unsloth/models/sentence_transformer.py @Etherll @danielhanchen
+/unsloth/save.py @rolandtannous @danielhanchen
+/unsloth/tokenizer_utils.py @mmathew23 @danielhanchen
+/unsloth/chat_templates.py @rolandtannous @danielhanchen
+/unsloth/ollama_template_mappers.py @rolandtannous @danielhanchen
+/unsloth/kernels/moe/*.py @Datta0
+/unsloth/import_fixes.py @danielhanchen
+/unsloth/device_type.py @danielhanchen
+/unsloth/_auto_install.py @danielhanchen
+/unsloth/dataprep/*.py @danielhanchen
+/unsloth/kernels/cross_entropy_loss.py @danielhanchen
+/unsloth/kernels/fast_lora.py @danielhanchen
+/unsloth/kernels/flex_attention.py @danielhanchen
+/unsloth/kernels/fp8.py @Datta0
+/unsloth/kernels/geglu.py @danielhanchen
+/unsloth/kernels/layernorm.py @danielhanchen
+/unsloth/kernels/rms_layernorm.py @danielhanchen
+/unsloth/kernels/rope_embedding.py @danielhanchen
+/unsloth/kernels/swiglu.py @danielhanchen
+/unsloth/kernels/utils.py @danielhanchen @Datta0
+/unsloth/models/_utils.py @danielhanchen @mmathew23
+/unsloth/models/cohere.py @danielhanchen
+/unsloth/models/dpo.py @danielhanchen
+/unsloth/models/falcon_h1.py @danielhanchen
+/unsloth/models/gemma.py @danielhanchen
+/unsloth/models/gemma2.py @danielhanchen
+/unsloth/models/glm4_moe.py @Datta0
+/unsloth/models/granite.py @danielhanchen
+/unsloth/models/llama4.py @danielhanchen
+/unsloth/models/loader_utils.py @Datta0 @danielhanchen
+/unsloth/models/mapper.py @danielhanchen
+/unsloth/models/mistral.py @danielhanchen
+/unsloth/models/qwen2.py @danielhanchen
+/unsloth/models/qwen3.py @Datta0
+/unsloth/models/qwen3_moe.py @Datta0
+/unsloth/models/vision.py @mmathew23 @danielhanchen
+/unsloth/utils/attention_dispatch.py @mmathew23
+/unsloth/utils/hf_hub.py @mmathew23
+/unsloth/utils/packing.py @mmathew23
+
+/cli/ @rolandtannous @Manan17
+/studio/frontend/ @Shine1i @rolandtannous @Manan17
+/studio/frontend/public/ @Shine1i
+/studio/backend/ @rolandtannous
+/studio/backend/core/data_recipe/ @rolandtannous
+/studio/backend/tests/ @rolandtannous @danielhanchen
+/tests/ @rolandtannous @danielhanchen
+/scripts/ @rolandtannous @danielhanchen

.github/FUNDING.yml

@@ -1,9 +1,9 @@
 # These are supported funding model platforms
 
-github: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]
+github: unslothai
 patreon: # Replace with a single Patreon username
 open_collective: # Replace with a single Open Collective username
-ko_fi: unsloth
+ko_fi: # unsloth
 tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
 community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
 liberapay: # Replace with a single Liberapay username

.github/ISSUE_TEMPLATE/bug---issue.md

@@ -0,0 +1,22 @@
+---
+name: Bug / Issue
+about: Bug / Issue
+title: "[Bug] Please fill in your issue title here."
+labels: bug
+assignees: ''
+
+---
+Note: Please do not remove the questions. Answer beside them.
+1. Did you update? `pip install --upgrade unsloth unsloth_zoo`
+2. `Colab` or `Kaggle` or local / cloud
+3. Number GPUs used, use `nvidia-smi`
+4. Which notebook? Please link!
+5. Which Unsloth version, TRL version, transformers version, PyTorch version?
+6. Which trainer? `SFTTrainer`, `GRPOTrainer` etc
+
+```python
+Put Minimal code to reproduce error here ###Remove Hugging Face token###
+###Please make sure to check formatting properly, edit if needed.###
+```
+
+🦥 You can also ask via our Reddit page: https://reddit.com/r/unsloth/

.github/ISSUE_TEMPLATE/feature-request.md

@@ -0,0 +1,21 @@
+---
+name: Feature Request
+about: New features, model support, ideas
+title: "[Feature]"
+labels: feature request
+assignees: ''
+
+---
+
+For new models, have you tried:
+```python
+from unsloth import FastModel
+model, tokenizer = FastModel.from_pretrained(
+    "microsoft/Phi-4-multimodal-instruct",
+    trust_remote_code = True,
+)
+from transformers import AutoModelForSequenceClassification
+model, tokenizer = FastModel.from_pretrained(
+    auto_model = AutoModelForSequenceClassification,
+)
+```

.github/dependabot.yml

@@ -0,0 +1,27 @@
+---
+version: 2
+updates:
+  - package-ecosystem: "github-actions"
+    directory: "/"
+    schedule:
+      interval: "weekly"
+    groups:
+      actions:
+        patterns: ["*"]
+
+  - package-ecosystem: "bun"
+    directory: "/studio/frontend"
+    schedule:
+      interval: "weekly"
+    groups:
+      bun-frontend:
+        patterns: ["*"]
+
+  - package-ecosystem: "npm"
+    directory: "/studio/backend/core/data_recipe/oxc-validator"
+    schedule:
+      interval: "weekly"
+    groups:
+      npm-oxc-validator:
+        patterns: ["*"]
+...

.github/workflows/stale.yml

@@ -0,0 +1,37 @@
+name: 'Inactive Issue Pinger'
+
+on:
+  schedule:
+    - cron: '30 5 * * *' # Runs at 5:30 UTC every day
+
+jobs:
+  stale:
+    runs-on: ubuntu-latest
+    permissions:
+      issues: write
+
+    steps:
+      - uses: actions/stale@v10
+        with:
+          # The message to post on stale issues.
+          # This message will ping the issue author.
+          # Note: The stale bot action does not currently support a direct placeholder for the last commenter.
+          # As a workaround, this message encourages any participant to reply.
+          stale-issue-message: >
+            Is this issue still important to you?
+            Apologies in advance we might have missed this issue as well.
+            For faster response times, please post on our Reddit server - https://www.reddit.com/r/unsloth or our Discord - https://discord.com/invite/unsloth 
+
+          # The number of days of inactivity before an issue is considered stale.
+          days-before-issue-stale: 9999
+
+          # Set to -1 to never close stale issues.
+          days-before-issue-close: -1
+
+          # A label to apply to stale issues.
+          stale-issue-label: 'inactive'
+
+          # The number of operations to perform per run to avoid rate limiting.
+          operations-per-run: 500
+
+          enable-statistics: false

.gitignore

@@ -1,7 +1,17 @@
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
-*$py.class
+*.class
+unsloth_compiled_cache/
+# ML artifacts (large files)
+feature/
+outputs/
+exports/
+/datasets/
+studio/backend/assets/datasets/
+unsloth_training_checkpoints/
+*.gguf
+*.safetensors
 
 # C extensions
 *.so
@@ -94,6 +104,12 @@ ipython_config.py
 #   install all needed dependencies.
 #Pipfile.lock
 
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#uv.lock
+
 # poetry
 #   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
 #   This is especially recommended for binary packages to ensure reproducibility, and is more
@@ -106,8 +122,10 @@ ipython_config.py
 #pdm.lock
 #   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
 #   in version control.
-#   https://pdm.fming.dev/#use-with-ide
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
 .pdm.toml
+.pdm-python
+.pdm-build/
 
 # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
 __pypackages__/
@@ -127,6 +145,9 @@ venv/
 ENV/
 env.bak/
 venv.bak/
+.venv_overlay/
+.venv_t5/
+environment.yaml
 
 # Spyder project settings
 .spyderproject
@@ -158,3 +179,40 @@ cython_debug/
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
+
+# Ruff stuff:
+.ruff_cache/
+.pre-commit-cache/
+
+# PyPI configuration file and IDE/Editors
+.pypirc
+.vscode
+.idea/
+.claude/
+*.swp
+*.swo
+
+# oh-my-codex
+.omx/
+
+# Firebase
+firebase-debug.log
+
+# Other
+resources/
+tmp/
+**/node_modules/
+auth.db
+
+# Local working docs
+**/CLAUDE.md
+**/claude.md
+**/AGENT.md
+**/agent.md
+docs/canvas-lab-architecture.md
+log_rtx.txt
+log.txt
+setup_leo.sh
+server.pid
+*.log
+package-lock.json

.pre-commit-ci.yaml

@@ -0,0 +1,6 @@
+ci:
+  autofix_prs: true
+  autofix_prs_limit: 5
+  autoupdate_schedule: monthly
+  autoupdate_commit_msg: "chore: pre-commit autoupdate"
+  skip: []

.pre-commit-config.yaml

@@ -0,0 +1,18 @@
+repos:
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.15.10
+    hooks:
+      - id: ruff
+        args:
+          - --fix
+          - --exit-non-zero-on-fix
+        exclude: '\.ipynb$'
+  - repo: local
+    hooks:
+      - id: ruff-format-with-kwargs
+        name: Ruff format with kwarg spacing
+        entry: scripts/run_ruff_format.py
+        language: python
+        types: [python]
+        additional_dependencies:
+          - ruff==0.6.9

CODE_OF_CONDUCT.md

@@ -0,0 +1,132 @@
+
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+We as members, contributors, and leaders pledge to make participation in our
+community a harassment-free experience for everyone, regardless of age, body
+size, visible or invisible disability, ethnicity, sex characteristics, gender
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+nationality, personal appearance, race, caste, color, religion, or sexual
+identity and orientation.
+
+We pledge to act and interact in ways that contribute to an open, welcoming,
+diverse, inclusive, and healthy community.
+
+## Our Standards
+
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+community include:
+
+* Demonstrating empathy and kindness toward other people
+* Being respectful of differing opinions, viewpoints, and experiences
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+
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+## Enforcement Responsibilities
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+
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+
+## Scope
+
+This Code of Conduct applies within all community spaces, and also applies when
+an individual is officially representing the community in public spaces.
+Examples of representing our community include using an official e-mail address,
+posting via an official social media account, or acting as an appointed
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+All community leaders are obligated to respect the privacy and security of the
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+
+## Enforcement Guidelines
+
+Community leaders will follow these Community Impact Guidelines in determining
+the consequences for any action they deem in violation of this Code of Conduct:
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+### 1. Correction
+
+**Community Impact**: Use of inappropriate language or other behavior deemed
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+
+**Consequence**: A private, written warning from community leaders, providing
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+### 2. Warning
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+
+### 3. Temporary Ban
+
+**Community Impact**: A serious violation of community standards, including
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+
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+Violating these terms may lead to a permanent ban.
+
+### 4. Permanent Ban
+
+**Community Impact**: Demonstrating a pattern of violation of community
+standards, including sustained inappropriate behavior, harassment of an
+individual, or aggression toward or disparagement of classes of individuals.
+
+**Consequence**: A permanent ban from any sort of public interaction within the
+community.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage],
+version 2.1, available at
+[https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1].
+
+Community Impact Guidelines were inspired by
+[Mozilla's code of conduct enforcement ladder][Mozilla CoC].
+
+For answers to common questions about this code of conduct, see the FAQ at
+[https://www.contributor-covenant.org/faq][FAQ]. Translations are available at
+[https://www.contributor-covenant.org/translations][translations].
+
+[homepage]: https://www.contributor-covenant.org
+[v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html
+[Mozilla CoC]: https://github.com/mozilla/diversity
+[FAQ]: https://www.contributor-covenant.org/faq
+[translations]: https://www.contributor-covenant.org/translations

CONTRIBUTING.md

@@ -0,0 +1,29 @@
+# 🦥 Contributing to Unsloth
+
+Thank you for not only using Unsloth but also for being interested in helping out! We value all contributions, whether they come in the form of code, ideas, support for others or just by simply spreading the word of Unsloth! 💕
+
+- **[Support the Community](https://github.com/unslothai/unsloth/issues)**: Answer questions, review pull requests, or assist others in discussions.
+- **Fix Bugs**: Identify and resolve issues with the existing codebase.
+- **Submit Ideas**: Request new features or share enhancements you'd like to see.
+- **Develop Features**: Implement new functionality or improve existing tools which can be done via PRs.
+- **[Improve Documentation](https://docs.unsloth.ai/)**: Help by creating guides, FAQs, or enhancing clarity.
+
+One of the best ways to support us is by spreading the word about Unsloth! Share how it’s powering your amazing projects in blog posts or social media, and inspire others to explore its potential. Even a simple star on our repo goes a long way in showing your support and helping the community grow. 🌟
+
+## Submitting Issues
+If you find a bug or have a feature idea, we’d love to hear from you! Here’s how to make your submission stand out:
+
+### Reporting Bugs
+1. **Search First**: Check if the issue has already been reported using GitHub’s search bar under Issues.
+2. **Details Matter**: Is this on Google Colab, Kaggle, or on another platform service? Are you using Unsloth's official notebook? Include your OS, Python version, and other relevant details. For bugs, a concise code snippet that reproduces the issue is incredibly helpful.
+3. **Be Thorough**: Attach screenshots, traceback logs, or any additional information that might speed up resolution.
+
+## Spread the Word
+Your support extends beyond code:
+- Spread the word by writing about Unsloth in blogs or social media.
+- Share how Unsloth powers your projects.
+- Star our repository to show your appreciation.
+
+Finally, please be mindful of our [Code of Conduct](https://github.com/unslothai/unsloth/blob/main/CODE_OF_CONDUCT.md) to ensure a welcoming and inclusive environment for everyone.
+
+Thank you so much for reading and we hope you have lots of fun using Unsloth! 🦥

COPYING

@@ -0,0 +1,664 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU Affero General Public License is a free, copyleft license for
+software and other kinds of works, specifically designed to ensure
+cooperation with the community in the case of network server software.
+
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+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU Affero General Public License as published
+    by the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU Affero General Public License for more details.
+
+    You should have received a copy of the GNU Affero General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If your software can interact with users remotely through a computer
+network, you should also make sure that it provides a way for users to
+get its source.  For example, if your program is a web application, its
+interface could display a "Source" link that leads users to an archive
+of the code.  There are many ways you could offer source, and different
+solutions will be better for different programs; see section 13 for the
+specific requirements.
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU AGPL, see
+<https://www.gnu.org/licenses/>.
+
+Files under unsloth/*, tests/*, scripts/* are Apache 2.0 licensed.
+Files under studio/*, unsloth_cli/* which is optional to install are AGPLv3 licensed.

LICENSE

@@ -186,7 +186,9 @@
       same "printed page" as the copyright notice for easier
       identification within third-party archives.
 
-   Copyright [yyyy] [name of copyright owner]
+   Copyright [2024-] [Unsloth AI. Inc team, Daniel Han-Chen & Michael Han-Chen]
+   Files under unsloth/*, tests/*, scripts/* are Apache 2.0 licensed.
+   Files under studio/*, unsloth_cli/* which is optional to install are AGPLv3 licensed.
 
    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.

README.md

@@ -1,441 +1,266 @@
-<p align="center">
-  <picture>
-    <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/shimmyshimmer/unsloth/main/images/unsloth%20logo%20white%20text.png">
-    <source media="(prefers-color-scheme: light)" srcset="https://raw.githubusercontent.com/shimmyshimmer/unsloth/main/images/unsloth%20logo%20black%20text.png">
-    <img alt="unsloth logo" src="./images/unsloth%20logo%20black%20text.png" height="120" style="max-width: 100%;">
-  </picture>
-</p>
-<p align="center">
-  <a href="https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing"><img src="./images/Free version button.png" height="50"></a>
-  <a href="https://discord.gg/u54VK8m8tk"><img src="./images/Discord button.png" height="50"></a>
-  <a href="https://ko-fi.com/unsloth"><img src="./images/Kofi button.png" height="50"></a>
-</p>
+<h1 align="center" style="margin:0;">
+  <a href="https://unsloth.ai/docs"><picture>
+    <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20white%20text.png">
+    <source media="(prefers-color-scheme: light)" srcset="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20black%20text.png">
+    <img alt="Unsloth logo" src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20logo%20black%20text.png" height="80" style="max-width:100%;">
+  </picture></a>
+</h1>
+<h3 align="center" style="margin: 0; margin-top: 0;">
+Unsloth Studio lets you run and train models locally.
+</h3>
 
-<h2 align="center">
-    Finetune Mistral, Llama 2-5x faster with 50% less memory!
-</h2>
+<p align="center">
+  <a href="#-features">Features</a> •
+  <a href="#-install">Quickstart</a> •
+  <a href="#-free-notebooks">Notebooks</a> •
+  <a href="https://unsloth.ai/docs">Documentation</a>
+</p>
 <br>
+<a href="https://unsloth.ai/docs/new/studio">
+<img alt="unsloth studio ui homepage" src="https://github.com/user-attachments/assets/53ae17a9-d975-44ef-9686-efb4ebd0454d" style="max-width: 100%; margin-bottom: 0;"></a>
 
-| Llama 2 7b                    | Mistral 7b                  | CodeLlama 34b           | Llama 7b Kaggle 2x T4  |
-|-----------------------------|-----------------------------|-------------------------|------------------------|
-| **2.2x faster 43% less VRAM**     | **2.2x faster 62% less VRAM**     | **1.9x faster 27% less VRAM**  | **5.5x faster 44% less VRAM** |
-| [⭐Llama **free** Colab notebook](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing") | [⭐Mistral **free** Colab notebook](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing) | [CodeLlama A100 Colab notebook](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing) | [⭐Kaggle **free** Alpaca notebook](https://www.kaggle.com/danielhanchen/unsloth-alpaca-t4-ddp)
-| [Llama A100 Colab notebook](https://colab.research.google.com/drive/1YIPY_18xm-K0iJDgvNkRoJsgkPMPAO3G?usp=sharing) | [Mistral A100 Colab notebook](https://colab.research.google.com/drive/1SKrKGV-BZoU4kv5q3g0jtE_OhRgPtrrQ?usp=sharing) | 50+ more examples below! | [⭐Kaggle **free** Slim Orca notebook](https://www.kaggle.com/danielhanchen/unsloth-slimorca-t4-ddp) |
+## ⚡ Get started
 
-* **NEW!** [DPO](https://arxiv.org/abs/2305.18290) support. ⭐**Free!** DPO Zephyr, Mistral example! <a href="https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing"><img src="./images/Colab.png" height="20">  [More info](#DPO) on DPO
-* **NEW!** [TinyLlama 1.1b](https://github.com/jzhang38/TinyLlama) on 3T tokens! ⭐**Free!** example <a href="https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing"><img src="./images/Colab.png" height="20">
-* **NEW!** We're in 🤗 Huggingface's official docs! We're on the [SFT docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth) and the [DPO docs](https://huggingface.co/docs/trl/main/en/dpo_trainer#accelerate-dpo-fine-tuning-using-unsloth)!
-* Supports Llama, Yi, Mistral, CodeLlama, Qwen (llamafied), Deepseek and their derived models (Open Hermes etc).
-* All kernels written in [OpenAI's Triton](https://openai.com/research/triton) language. **Manual backprop engine**.
-* **0% loss in accuracy** - no approximation methods - all exact.
-* No change of hardware. Supports NVIDIA GPUs since 2018+. Minimum CUDA Capability 7.0 (V100, T4, Titan V, RTX 20, 30, 40x, A100, H100, L40 etc) [Check your GPU!](https://developer.nvidia.com/cuda-gpus) GTX 1070, 1080 works, but is slow.
-* Works on **Linux** and **Windows** via WSL.
-* **NEW!** Download 4 bit models 4x faster from 🤗 Huggingface! Eg: `unsloth/mistral-7b-bnb-4bit`
-* Supports 4bit and 16bit QLoRA / LoRA finetuning via [bitsandbytes](https://github.com/TimDettmers/bitsandbytes).
-* **NEW!** Want a UI for finetuning? Try [Llama-Factory](https://github.com/hiyouga/LLaMA-Factory) and use `--use_unsloth`!
-* Open source trains 5x faster - see [Unsloth Pro](https://unsloth.ai/) for **30x faster training**!
-
-| 1 A100 40GB  | 🤗 Hugging Face | Flash Attention | 🦥 Unsloth Open Source | [🦥 Unsloth Pro](https://unsloth.ai/pricing) |
-|--------------|--------------|-----------------|---------------------|-----------------|
-| Alpaca       | 1x           | 1.04x           | 1.98x               | **15.64x**      |
-| LAION Chip2  | 1x           | 0.92x           | 1.61x               | **20.73x**      |
-| OASST        | 1x           | 1.19x           | 2.17x               | **14.83x**      |
-| Slim Orca    | 1x           | 1.18x           | 2.22x               | **14.82x**      |
-
-Join our [Discord](https://discord.gg/nsS4V5Z6ge)!
-
-<img src="./images/unsloth made with love.png" width="200" />
-If you trained a model with 🦥 Unsloth, we made a cool sticker if you want to use it!
-
-# Installation Instructions - Conda
-Select either `pytorch-cuda=11.8` for CUDA 11.8 or `pytorch-cuda=12.1` for CUDA 12.1.
+#### macOS, Linux, WSL:
 ```bash
-conda install cudatoolkit xformers bitsandbytes pytorch pytorch-cuda=12.1 \
-  -c pytorch -c nvidia -c xformers -c conda-forge -y
-pip install "unsloth[conda] @ git+https://github.com/unslothai/unsloth.git"
+curl -fsSL https://unsloth.ai/install.sh | sh
 ```
-
-# Installation Instructions - Pip
-Do **NOT** use this if you have Anaconda. You must use the Conda install method, or else stuff will BREAK.
-
-1. Find your CUDA version via
-```python
-import torch; torch.version.cuda
+#### Windows:
+```powershell
+irm https://unsloth.ai/install.ps1 | iex
 ```
-2. For Pytorch 2.1.0: You can update Pytorch via Pip (interchange `cu121` / `cu118`). Go to https://pytorch.org/ to learn more. Select either `cu118` for CUDA 11.8 or `cu121` for CUDA 12.1. If you have a RTX 3060 or higher (A100, H100 etc), use the `"ampere"` path. For Pytorch 2.1.1: got to step 3.
+#### Community:
+
+- [Discord](https://discord.gg/unsloth)
+- [𝕏 (Twitter)](https://x.com/UnslothAI)
+- [Reddit](https://reddit.com/r/unsloth)
+
+## ⭐ Features
+Unsloth Studio (Beta) lets you run and train text, [audio](https://unsloth.ai/docs/basics/text-to-speech-tts-fine-tuning), [embedding](https://unsloth.ai/docs/new/embedding-finetuning), [vision](https://unsloth.ai/docs/basics/vision-fine-tuning) models on Windows, Linux and macOS.
+
+### Inference
+* **Search + download + run models** including GGUF, LoRA adapters, safetensors
+* **Export models**: [Save or export](https://unsloth.ai/docs/new/studio/export) models to GGUF, 16-bit safetensors and other formats.
+* **Tool calling**: Support for [self-healing tool calling](https://unsloth.ai/docs/new/studio/chat#auto-healing-tool-calling) and web search
+* **[Code execution](https://unsloth.ai/docs/new/studio/chat#code-execution)**: lets LLMs test code in Claude artifacts and sandbox environments
+* [Auto-tune inference parameters](https://unsloth.ai/docs/new/studio/chat#auto-parameter-tuning) and customize chat templates.
+* We work directly with teams behind [gpt-oss](https://docs.unsloth.ai/new/gpt-oss-how-to-run-and-fine-tune#unsloth-fixes-for-gpt-oss), [Qwen3](https://www.reddit.com/r/LocalLLaMA/comments/1kaodxu/qwen3_unsloth_dynamic_ggufs_128k_context_bug_fixes/), [Llama 4](https://github.com/ggml-org/llama.cpp/pull/12889), [Mistral](models/tutorials/devstral-how-to-run-and-fine-tune.md), [Gemma 1-3](https://news.ycombinator.com/item?id=39671146), and [Phi-4](https://unsloth.ai/blog/phi4), where we’ve fixed bugs that improve model accuracy.
+* Upload images, audio, PDFs, code, DOCX and more file types to chat with.
+### Training
+* Train and RL **500+ models** up to **2x faster** with up to **70% less VRAM**, with no accuracy loss.
+* Custom Triton and mathematical **kernels**. See some collabs we did with [PyTorch](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/fp8-reinforcement-learning) and [Hugging Face](https://unsloth.ai/docs/new/faster-moe).
+* **Data Recipes**: [Auto-create datasets](https://unsloth.ai/docs/new/studio/data-recipe) from **PDF, CSV, DOCX** etc. Edit data in a visual-node workflow.
+* **[Reinforcement Learning](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide)** (RL): The most efficient [RL](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide) library, using **80% less VRAM** for GRPO, [FP8](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/fp8-reinforcement-learning) etc.
+* Supports full fine-tuning, RL, pretraining, 4-bit, 16-bit and, FP8 training.
+* **Observability**: Monitor training live, track loss and GPU usage and customize graphs.
+* [Multi-GPU](https://unsloth.ai/docs/basics/multi-gpu-training-with-unsloth) training is supported, with major improvements coming soon.
+
+## 📥 Install
+Unsloth can be used in two ways: through **[Unsloth Studio](https://unsloth.ai/docs/new/studio/)**, the web UI, or through **Unsloth Core**, the code-based version. Each has different requirements.
+
+### Unsloth Studio (web UI)
+Unsloth Studio (Beta) works on **Windows, Linux, WSL** and **macOS**.
+
+* **CPU:** Supported for Chat and Data Recipes currently
+* **NVIDIA:** Training works on RTX 30/40/50, Blackwell, DGX Spark, Station and more
+* **macOS:** Currently supports chat and Data Recipes. **MLX training** is coming very soon
+* **AMD:** Chat + Data works. Train with [Unsloth Core](#unsloth-core-code-based). Studio support is out soon.
+* **Coming soon:** Training support for Apple MLX, AMD, and Intel.
+* **Multi-GPU:** Available now, with a major upgrade on the way
+
+#### macOS, Linux, WSL:
 ```bash
-pip install --upgrade --force-reinstall --no-cache-dir torch==2.1.0 triton \
-  --index-url https://download.pytorch.org/whl/cu121
+curl -fsSL https://unsloth.ai/install.sh | sh
 ```
+#### Windows:
+```powershell
+irm https://unsloth.ai/install.ps1 | iex
+```
+
+#### Launch
 ```bash
-pip install "unsloth[cu118] @ git+https://github.com/unslothai/unsloth.git"
-pip install "unsloth[cu121] @ git+https://github.com/unslothai/unsloth.git"
-pip install "unsloth[cu118_ampere] @ git+https://github.com/unslothai/unsloth.git"
-pip install "unsloth[cu121_ampere] @ git+https://github.com/unslothai/unsloth.git"
+unsloth studio -H 0.0.0.0 -p 8888
 ```
-3. For Pytorch 2.1.1: Use the `"ampere"` path for newer RTX 30xx GPUs or higher.
+
+#### Update
+To update, use the same install commands as above. Or run (does not work on Windows):
 ```bash
-pip install --upgrade --force-reinstall --no-cache-dir torch==2.1.1 triton \
-  --index-url https://download.pytorch.org/whl/cu121
+unsloth studio update
 ```
+
+#### Docker
+Use our [Docker image](https://hub.docker.com/r/unsloth/unsloth) ```unsloth/unsloth``` container. Run:
 ```bash
-pip install "unsloth[cu118_torch211] @ git+https://github.com/unslothai/unsloth.git"
-pip install "unsloth[cu121_torch211] @ git+https://github.com/unslothai/unsloth.git"
-pip install "unsloth[cu118_ampere_torch211] @ git+https://github.com/unslothai/unsloth.git"
-pip install "unsloth[cu121_ampere_torch211] @ git+https://github.com/unslothai/unsloth.git"
-```
-4. We're working on Pytorch 2.1.2 support.
-5. If you get errors, try the below first, then go back to step 1:
+docker run -d -e JUPYTER_PASSWORD="mypassword" \
+  -p 8888:8888 -p 8000:8000 -p 2222:22 \
+  -v $(pwd)/work:/workspace/work \
+  --gpus all \
+  unsloth/unsloth
+  ```
+
+#### Developer, Nightly, Uninstall
+To see developer, nightly and uninstallation etc. instructions, see [advanced installation](#-advanced-installation).
+
+### Unsloth Core (code-based)
+#### Linux, WSL:
 ```bash
-pip install --upgrade pip
+curl -LsSf https://astral.sh/uv/install.sh | sh
+uv venv unsloth_env --python 3.13
+source unsloth_env/bin/activate
+uv pip install unsloth --torch-backend=auto
 ```
-
-# Documentation
-We support Huggingface's TRL, Trainer, Seq2SeqTrainer or even Pytorch code!
-
-We're in 🤗 Huggingface's official docs! We're on the [SFT docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth) and the [DPO docs](https://huggingface.co/docs/trl/main/en/dpo_trainer#accelerate-dpo-fine-tuning-using-unsloth)!
-
-```python
-from unsloth import FastLanguageModel
-import torch
-from trl import SFTTrainer
-from transformers import TrainingArguments
-from datasets import load_dataset
-max_seq_length = 2048 # Supports RoPE Scaling interally, so choose any!
-# Get LAION dataset
-url = "https://huggingface.co/datasets/laion/OIG/resolve/main/unified_chip2.jsonl"
-dataset = load_dataset("json", data_files = {"train" : url}, split = "train")
-
-# 4bit pre quantized models we support - 4x faster downloading!
-fourbit_models = [
-    "unsloth/mistral-7b-bnb-4bit",
-    "unsloth/llama-2-7b-bnb-4bit",
-    "unsloth/llama-2-13b-bnb-4bit",
-    "unsloth/codellama-34b-bnb-4bit",
-    "unsloth/tinyllama-bnb-4bit",
-]
-# Load Llama model
-model, tokenizer = FastLanguageModel.from_pretrained(
-    model_name = "unsloth/mistral-7b-bnb-4bit", # Supports Llama, Mistral - replace this!
-    max_seq_length = max_seq_length,
-    dtype = None,
-    load_in_4bit = True,
-)
-
-# Do model patching and add fast LoRA weights
-model = FastLanguageModel.get_peft_model(
-    model,
-    r = 16,
-    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
-                      "gate_proj", "up_proj", "down_proj",],
-    lora_alpha = 16,
-    lora_dropout = 0, # Supports any, but = 0 is optimized
-    bias = "none",    # Supports any, but = "none" is optimized
-    use_gradient_checkpointing = True,
-    random_state = 3407,
-    max_seq_length = max_seq_length,
-)
-
-trainer = SFTTrainer(
-    model = model,
-    train_dataset = dataset,
-    dataset_text_field = "text",
-    max_seq_length = max_seq_length,
-    tokenizer = tokenizer,
-    args = TrainingArguments(
-        per_device_train_batch_size = 2,
-        gradient_accumulation_steps = 4,
-        warmup_steps = 10,
-        max_steps = 60,
-        fp16 = not torch.cuda.is_bf16_supported(),
-        bf16 = torch.cuda.is_bf16_supported(),
-        logging_steps = 1,
-        output_dir = "outputs",
-        optim = "adamw_8bit",
-        seed = 3407,
-    ),
-)
-trainer.train()
+#### Windows:
+```powershell
+winget install -e --id Python.Python.3.13
+winget install --id=astral-sh.uv  -e
+uv venv unsloth_env --python 3.13
+.\unsloth_env\Scripts\activate
+uv pip install unsloth --torch-backend=auto
 ```
+For Windows, `pip install unsloth` works only if you have PyTorch installed. Read our [Windows Guide](https://unsloth.ai/docs/get-started/install/windows-installation).
+You can use the same Docker image as Unsloth Studio.
 
-<a name="DPO"></a>
-# DPO (Direct Preference Optimization) Support
-DPO, PPO, Reward Modelling all seem to work as per 3rd party independent testing from [Llama-Factory](https://github.com/hiyouga/LLaMA-Factory). We have a preliminary Google Colab notebook for reproducing Zephyr on Tesla T4 here: [notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing).
+#### AMD, Intel:
+For RTX 50x, B200, 6000 GPUs: `uv pip install unsloth --torch-backend=auto`. Read our guides for: [Blackwell](https://unsloth.ai/docs/blog/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth) and [DGX Spark](https://unsloth.ai/docs/blog/fine-tuning-llms-with-nvidia-dgx-spark-and-unsloth). <br>
+To install Unsloth on **AMD** and **Intel** GPUs, follow our [AMD Guide](https://unsloth.ai/docs/get-started/install/amd) and [Intel Guide](https://unsloth.ai/docs/get-started/install/intel).
 
-We're in 🤗 Huggingface's official docs! We're on the [SFT docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth) and the [DPO docs](https://huggingface.co/docs/trl/main/en/dpo_trainer#accelerate-dpo-fine-tuning-using-unsloth)!
+## 📒 Free Notebooks
 
-```python
-from unsloth import FastLanguageModel, PatchDPOTrainer
-PatchDPOTrainer()
-import torch
-from transformers import TrainingArguments
-from trl import DPOTrainer
+Train for free with our notebooks. You can use our new [free Unsloth Studio notebook](https://colab.research.google.com/github/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb) to run and train models for free in a web UI.
+Read our [guide](https://unsloth.ai/docs/get-started/fine-tuning-llms-guide). Add dataset, run, then deploy your trained model.
 
-model, tokenizer = FastLanguageModel.from_pretrained(
-    model_name = "unsloth/zephyr-sft-bnb-4bit",
-    max_seq_length = max_seq_length,
-    dtype = None,
-    load_in_4bit = True,
-)
+| Model | Free Notebooks | Performance | Memory use |
+|-----------|---------|--------|----------|
+| **Gemma 4 (E2B)**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma4_(E2B)-Vision.ipynb)               | 1.5x faster | 50% less |
+| **Qwen3.5 (4B)**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_5_(4B)_Vision.ipynb)               | 1.5x faster | 60% less |
+| **gpt-oss (20B)**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-Fine-tuning.ipynb)               | 2x faster | 70% less |
+| **Qwen3.5 GSPO**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_5_(4B)_Vision_GRPO.ipynb)               | 2x faster | 70% less |
+| **gpt-oss (20B): GRPO**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-GRPO.ipynb)               | 2x faster | 80% less |
+| **Qwen3: Advanced GRPO**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_(4B)-GRPO.ipynb)               | 2x faster | 70% less |
+| **embeddinggemma (300M)**    | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/EmbeddingGemma_(300M).ipynb)               | 2x faster | 20% less |
+| **Mistral Ministral 3 (3B)**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Ministral_3_VL_(3B)_Vision.ipynb)               | 1.5x faster | 60% less |
+| **Llama 3.1 (8B) Alpaca**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Alpaca.ipynb)               | 2x faster | 70% less |
+| **Llama 3.2 Conversational**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb)               | 2x faster | 70% less |
+| **Orpheus-TTS (3B)**     | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Orpheus_(3B)-TTS.ipynb)               | 1.5x faster | 50% less |
 
-# Do model patching and add fast LoRA weights
-model = FastLanguageModel.get_peft_model(
-    model,
-    r = 64,
-    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
-                      "gate_proj", "up_proj", "down_proj",],
-    lora_alpha = 64,
-    lora_dropout = 0, # Supports any, but = 0 is optimized
-    bias = "none",    # Supports any, but = "none" is optimized
-    use_gradient_checkpointing = True,
-    random_state = 3407,
-    max_seq_length = max_seq_length,
-)
+- See all our notebooks for: [Kaggle](https://github.com/unslothai/notebooks?tab=readme-ov-file#-kaggle-notebooks), [GRPO](https://unsloth.ai/docs/get-started/unsloth-notebooks#grpo-reasoning-rl-notebooks), [TTS](https://unsloth.ai/docs/get-started/unsloth-notebooks#text-to-speech-tts-notebooks), [embedding](https://unsloth.ai/docs/new/embedding-finetuning) & [Vision](https://unsloth.ai/docs/get-started/unsloth-notebooks#vision-multimodal-notebooks)
+- See [all our models](https://unsloth.ai/docs/get-started/unsloth-model-catalog) and [all our notebooks](https://unsloth.ai/docs/get-started/unsloth-notebooks)
+- See detailed documentation for Unsloth [here](https://unsloth.ai/docs)
 
-dpo_trainer = DPOTrainer(
-    model = model,
-    ref_model = None,
-    args = TrainingArguments(
-        per_device_train_batch_size = 4,
-        gradient_accumulation_steps = 8,
-        warmup_ratio = 0.1,
-        num_train_epochs = 3,
-        fp16 = not torch.cuda.is_bf16_supported(),
-        bf16 = torch.cuda.is_bf16_supported(),
-        logging_steps = 1,
-        optim = "adamw_8bit",
-        seed = 42,
-        output_dir = "outputs",
-    ),
-    beta = 0.1,
-    train_dataset = YOUR_DATASET_HERE,
-    # eval_dataset = YOUR_DATASET_HERE,
-    tokenizer = tokenizer,
-    max_length = 1024,
-    max_prompt_length = 512,
-)
-dpo_trainer.train()
-```
+## 🦥 Unsloth News
+- **Qwen3.6**: Qwen3.6-35B-A3B can now be trained and run in Unsloth Studio. [Blog](https://unsloth.ai/docs/models/qwen3.6)
+- **Gemma 4**: Run and train Google’s new models directly in Unsloth. [Blog](https://unsloth.ai/docs/models/gemma-4)
+- **Introducing Unsloth Studio**: our new web UI for running and training LLMs. [Blog](https://unsloth.ai/docs/new/studio)
+- **Qwen3.5** - 0.8B, 2B, 4B, 9B, 27B, 35-A3B, 112B-A10B are now supported. [Guide + notebooks](https://unsloth.ai/docs/models/qwen3.5/fine-tune)
+- Train **MoE LLMs 12x faster** with 35% less VRAM - DeepSeek, GLM, Qwen and gpt-oss. [Blog](https://unsloth.ai/docs/new/faster-moe)
+- **Embedding models**: Unsloth now supports ~1.8-3.3x faster embedding fine-tuning. [Blog](https://unsloth.ai/docs/new/embedding-finetuning) • [Notebooks](https://unsloth.ai/docs/get-started/unsloth-notebooks#embedding-models)
+- New **7x longer context RL** vs. all other setups, via our new batching algorithms. [Blog](https://unsloth.ai/docs/new/grpo-long-context)
+- New RoPE & MLP **Triton Kernels** & **Padding Free + Packing**: 3x faster training & 30% less VRAM. [Blog](https://unsloth.ai/docs/new/3x-faster-training-packing)
+- **500K Context**: Training a 20B model with >500K context is now possible on an 80GB GPU. [Blog](https://unsloth.ai/docs/blog/500k-context-length-fine-tuning)
+- **FP8 & Vision RL**: You can now do FP8 & VLM GRPO on consumer GPUs. [FP8 Blog](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/fp8-reinforcement-learning) • [Vision RL](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/vision-reinforcement-learning-vlm-rl)
+- **gpt-oss** by OpenAI: Read our [RL blog](https://unsloth.ai/docs/models/gpt-oss-how-to-run-and-fine-tune/gpt-oss-reinforcement-learning), [Flex Attention](https://unsloth.ai/docs/models/gpt-oss-how-to-run-and-fine-tune/long-context-gpt-oss-training) blog and [Guide](https://unsloth.ai/docs/models/gpt-oss-how-to-run-and-fine-tune).
 
-# Support us!
-We're currently 2 brothers trying to make LLMs for everyone! It'll be super cool if you can support our work!!
-<a href="https://ko-fi.com/unsloth"><img src="./images/Kofi button.png" height="50"></a>
-
-# Future Milestones and limitations
-1. Support Mixtral.
-2. Supports all Mistral, Llama type models, but some are unoptimized (Qwen with biases)
-3. Dropout, bias in LoRA matrices are supported, just not optimized.
-
-# Performance comparisons on 1 Tesla T4 GPU:
-**Time taken for 1 epoch**
-
-One Tesla T4 on Google Colab
-`bsz = 2, ga = 4, max_grad_norm = 0.3, num_train_epochs = 1, seed = 3047, lr = 2e-4, wd = 0.01, optim = "adamw_8bit", schedule = "linear", schedule_steps = 10`
-
-| System | GPU | Alpaca (52K) | LAION OIG (210K) | Open Assistant (10K) | SlimOrca (518K) |
-| --- | --- | --- | --- | --- | --- |
-| Huggingface | 1 T4 | 23h 15m | 56h 28m | 8h 38m | 391h 41m |
-| Unsloth Open | 1 T4 | 13h 7m (1.8x) | 31h 47m (1.8x) | 4h 27m (1.9x) | 240h 4m (1.6x) |
-| Unsloth Pro | 1 T4 | 3h 6m (7.5x) | 5h 17m (10.7x) | 1h 7m (7.7x) | 59h 53m (6.5x) |
-| Unsloth Max | 1 T4 | 2h 39m (8.8x) | 4h 31m (12.5x) | 0h 58m (8.9x) | 51h 30m (7.6x) |
-
-**Peak Memory Usage**
-
-| System | GPU | Alpaca (52K) | LAION OIG (210K) | Open Assistant (10K) | SlimOrca (518K) |
-| --- | --- | --- | --- | --- | --- |
-| Huggingface | 1 T4 | 7.3GB | 5.9GB | 14.0GB | 13.3GB |
-| Unsloth Open | 1 T4 | 6.8GB | 5.7GB | 7.8GB | 7.7GB |
-| Unsloth Pro | 1 T4 | 6.4GB | 6.4GB | 6.4GB | 6.4GB |
-| Unsloth Max | 1 T4 | 11.4GB | 12.4GB | 11.9GB | 14.4GB |
-
-# Performance comparisons on 2 Tesla T4 GPUs via DDP:
-**Time taken for 1 epoch**
-
-Two Tesla T4s on Kaggle
-`bsz = 2, ga = 4, max_grad_norm = 0.3, num_train_epochs = 1, seed = 3047, lr = 2e-4, wd = 0.01, optim = "adamw_8bit", schedule = "linear", schedule_steps = 10`
-
-| System | GPU | Alpaca (52K) | LAION OIG (210K) | Open Assistant (10K) | SlimOrca (518K) * |
-| --- | --- | --- | --- | --- | --- |
-| Huggingface | 2 T4 | 84h 47m | 163h 48m | 30h 51m | 1301h 24m * |
-| Unsloth Pro | 2 T4 | 3h 20m (25.4x) | 5h 43m (28.7x) | 1h 12m (25.7x) | 71h 40m (18.1x) * |
-| Unsloth Max | 2 T4 | 3h 4m (27.6x) | 5h 14m (31.3x) | 1h 6m (28.1x) | 54h 20m (23.9x) * |
-
-**Peak Memory Usage on a Multi GPU System (2 GPUs)**
-
-| System | GPU | Alpaca (52K) | LAION OIG (210K) | Open Assistant (10K) | SlimOrca (518K) * |
-| --- | --- | --- | --- | --- | --- |
-| Huggingface | 2 T4 | 8.4GB \| 6GB | 7.2GB \| 5.3GB | 14.3GB \| 6.6GB | 10.9GB \| 5.9GB * |
-| Unsloth Pro | 2 T4 | 7.7GB \| 4.9GB | 7.5GB \| 4.9GB | 8.5GB \| 4.9GB | 6.2GB \| 4.7GB * |
-| Unsloth Max | 2 T4 | 10.5GB \| 5GB | 10.6GB \| 5GB | 10.6GB \| 5GB | 10.5GB \| 5GB * |
-
-* Slim Orca `bsz=1` for all benchmarks since `bsz=2` OOMs. We can handle `bsz=2`, but we benchmark it with `bsz=1` for consistency.
-
-# Llama-Factory 3rd party benchmarking
-
-| Method | Bits | TGS | GRAM | Speed |
-| --- | --- | --- | --- | --- |
-| HF | 16 | 2392 | 18GB | 100% |
-| HF+FA2 | 16 | 2954 | 17GB | 123% |
-| Unsloth+FA2 | 16 | 4007 | 16GB | **168%** |
-| HF | 4 | 2415 | 9GB | 101% |
-| Unsloth+FA2 | 4 | 3726 | 7GB | **160%** |
-
-[Link](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-Comparison) to performance table. TGS: tokens per GPU per second. Model: LLaMA2-7B. GPU: NVIDIA A100 * 1. Batch size: 4. Gradient accumulation: 2. LoRA rank: 8. Max length: 1024.
-
-# How did we make it faster?
-Manual autograd, Triton kernels etc. See our [Benchmark Breakdown](https://unsloth.ai/blog/mistral-benchmark) for more info!
-
-# Troubleshooting
-1. Sometimes `bitsandbytes` or `xformers` does not link properly. Try running:
+## 📥 Advanced Installation
+The below advanced instructions are for Unsloth Studio. For Unsloth Core advanced installation, [view our docs](https://unsloth.ai/docs/get-started/install/pip-install#advanced-pip-installation).
+#### Developer installs: macOS, Linux, WSL:
 ```bash
-!ldconfig /usr/lib64-nvidia
+git clone https://github.com/unslothai/unsloth
+cd unsloth
+./install.sh --local
+unsloth studio -H 0.0.0.0 -p 8888
+```
+Then to update :
+```bash
+unsloth studio update
 ```
-2. Windows is not supported as of yet - we rely on Xformers and Triton support, so until both packages support Windows officially, Unsloth will then support Windows.
 
-3. If it doesn't install - maybe try updating `pip`.
+#### Developer installs: Windows PowerShell:
+```powershell
+git clone https://github.com/unslothai/unsloth.git
+cd unsloth
+Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass
+.\install.ps1 --local
+unsloth studio -H 0.0.0.0 -p 8888
+```
+Then to update :
+```bash
+unsloth studio update
+```
 
+#### Nightly: MacOS, Linux, WSL:
+```bash
+git clone https://github.com/unslothai/unsloth
+cd unsloth
+git checkout nightly
+./install.sh --local
+unsloth studio -H 0.0.0.0 -p 8888
+```
+Then to launch every time:
+```bash
+unsloth studio -H 0.0.0.0 -p 8888
+```
 
-# Full benchmarking tables
-Click  "Code" for a fully reproducible example.
-"Unsloth Equal" is a preview of our PRO version, with code stripped out. All settings and the loss curve remains identical.
-| 1 A100 40GB | Hugging Face | Flash Attention 2 | Unsloth Open | Unsloth Equal | Unsloth Pro | Unsloth Max |
-|--------------|-------------|-------------|-----------------|--------------|---------------|-------------|
-| Alpaca       | 1x          | 1.04x       | 1.98x           | 2.48x        | 5.32x         | **15.64x**      |
-| code | [Code](https://colab.research.google.com/drive/1u4dBeM-0vGNVmmO6X7cScAut-Hyt4KDF?usp=sharing) |    [Code](https://colab.research.google.com/drive/1fgTOxpMbVjloQBvZyz4lF4BacKSZOB2A?usp=sharing) |    [Code](https://colab.research.google.com/drive/1YIPY_18xm-K0iJDgvNkRoJsgkPMPAO3G?usp=sharing) |    [Code](https://colab.research.google.com/drive/1ANW8EFL3LVyTD7Gq4TkheC1Z7Rxw-rHp?usp=sharing) | | |
-| seconds| 1040 | 1001 | 525 | 419 | 196 | 67  |
-| memory MB| 18235 | 15365 | 9631 | 8525 | | |
-| % saved| | 15.74 | 47.18 | 53.25 | | | |
+#### Nightly: Windows:
+Run in Windows Powershell:
+```bash
+git clone https://github.com/unslothai/unsloth.git
+cd unsloth
+git checkout nightly
+Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass
+.\install.ps1 --local
+unsloth studio -H 0.0.0.0 -p 8888
+```
+Then to launch every time:
+```bash
+unsloth studio -H 0.0.0.0 -p 8888
+```
 
+#### Uninstall
+You can uninstall Unsloth Studio by deleting its install folder usually located under `$HOME/.unsloth/studio` on Mac/Linux/WSL and `%USERPROFILE%\.unsloth\studio` on Windows. Using the `rm -rf` commands will **delete everything**, including your history, cache:
 
-| 1 A100 40GB | Hugging Face | Flash Attention 2 | Unsloth Open | Unsloth Equal | Unsloth Pro | Unsloth Max |
-|--------------|-------------|-------------|-----------------|--------------|---------------|-------------|
-| LAION Chip2  | 1x          | 0.92x       | 1.61x           | 1.84x        | 7.05x         | **20.73x**      |
-| code |[Code](https://colab.research.google.com/drive/1gjL1TaKwc_xv2TcxJC8QWEWBG1msh3g2?usp=sharing) |    [Code](https://colab.research.google.com/drive/15vlPjMr8xDj5BFhGdqunGaOQSMqXPEXU?usp=sharing) |    [Code](https://colab.research.google.com/drive/1zPwvf-BmHyHlPMBxDsY8zS0BnQ-KKbCc?usp=sharing) |    [Code](https://colab.research.google.com/drive/1X2uHy-arRsZxqWHvKHwwW102JaMwChD2?usp=sharing) | | |
-| seconds| 581  | 631  | 361 | 315 | 82  | 28  |
-| memory MB| 7763  | 8047  | 7763 | 6441 | | |
-| % saved| | -3.66 | 0.00  | 17.03 | | | |
+* ​ **MacOS, WSL, Linux:** `rm -rf ~/.unsloth/studio`
+* ​ **Windows (PowerShell):** `Remove-Item -Recurse -Force "$HOME\.unsloth\studio"`
 
+For more info, [see our docs](https://unsloth.ai/docs/new/studio/install#uninstall).
 
-| 1 A100 40GB | Hugging Face | Flash Attention 2 | Unsloth Open | Unsloth Equal | Unsloth Pro | Unsloth Max |
-|--------------|-------------|-------------|-----------------|--------------|---------------|-------------|
-| OASST        | 1x          | 1.19x       | 2.17x           | 2.66x        | 5.04x         | **14.83x**      |
-| code |[Code](https://colab.research.google.com/drive/10NzDreFbuWELGUuBv0MOoC7y3MBewaNx?usp=sharing) |    [Code](https://colab.research.google.com/drive/1TwdkJ1sHsuEH-kgeCPqSFeCpOnCfz6Ou?usp=sharing) |    [Code](https://colab.research.google.com/drive/1AkwjUkOF0XeRBMT_S8Uhh74kitEsZHla?usp=sharing) |    [Code](https://colab.research.google.com/drive/1roMkp2UjbeK2t3DkNz50cRs1MT92RPFT?usp=sharing) | | |
-| seconds| 1852 | 1558 | 852 | 696 | 367 | 125 |
-| memory MB| 26431 | 16565 | 12267| 11223| | |
-| % saved| | 37.33 | 53.59 | 57.54 | | |
+#### Deleting model files
 
-| 1 A100 40GB | Hugging Face | Flash Attention 2 | Unsloth Open | Unsloth Equal | Unsloth Pro | Unsloth Max |
-|--------------|-------------|-------------|-----------------|--------------|---------------|-------------|
-| Slim Orca    | 1x          | 1.18x       | 2.22x           | 2.64x        | 5.04x         | **14.82x**      |
-| code |[Code](https://colab.research.google.com/drive/1UNo1xsMl8YH7xnWnIVjDFnCAPfc0RGgu?usp=sharing) |    [Code](https://colab.research.google.com/drive/1zbphER-SKhbSWGjHTfnBLPFyTgIVvaeH?usp=sharing) |    [Code](https://colab.research.google.com/drive/156si33585iv4Uh-VILFglUmIMrNCNuc2?usp=sharing) |    [Code](https://colab.research.google.com/drive/1_mhZy7dfl9jEnJRuJBZJ5y3OwW06jgQA?usp=sharing) | | |
-| seconds| 1824 | 1545 | 821 | 691 | 362 | 123 |
-| memory MB| 24557 | 15681 | 10595| 9007 | | |
-| % saved| | 36.14 | 56.86 | 63.32 | | |
+You can delete old model files either from the bin icon in model search or by removing the relevant cached model folder from the default Hugging Face cache directory. By default, HF uses:
 
-### Mistral 7b
-| 1 A100 40GB | Hugging Face | Flash Attention 2 | Unsloth Open | Unsloth Equal | Unsloth Pro | Unsloth Max |
-|--------------|-------------|-------------|-----------------|--------------|---------------|-------------|
-| Mistral 7B Slim Orca  | 1x | 1.15x        | 2.15x        | 2.53x            | 4.61x         | **13.69x**         |
-| code | [Code](https://colab.research.google.com/drive/1mePk3KzwTD81hr5mcNcs_AX3Kbg_Ha0x?usp=sharing) | [Code](https://colab.research.google.com/drive/1dgHxjvTmX6hb0bPcLp26RXSE6_n9DKj7?usp=sharing) | [Code](https://colab.research.google.com/drive/1SKrKGV-BZoU4kv5q3g0jtE_OhRgPtrrQ?usp=sharing) | [Code](https://colab.research.google.com/drive/18yOiyX0T81mTwZqOALFSCX_tSAqju6aD?usp=sharing) | |
-| seconds      | 1813        | 1571        | 842             | 718          | 393           | 132         |
-| memory MB    | 32853       | 19385       | 12465           | 10271        |          |        |
-| % saved| | 40.99      | 62.06       | 68.74           |         |          |
+* ​ **MacOS, Linux, WSL:** `~/.cache/huggingface/hub/`
+* ​ **Windows:** `%USERPROFILE%\.cache\huggingface\hub\`
 
-### CodeLlama 34b
-| 1 A100 40GB | Hugging Face | Flash Attention 2 | Unsloth Open | Unsloth Equal | Unsloth Pro | Unsloth Max |
-|--------------|-------------|-------------|-----------------|--------------|---------------|-------------|
-| Code Llama 34B   | OOM ❌         | 0.99x        | 1.87x           | 2.61x        | 4.27x      | 12.82x      |
-| code | [Code](https://colab.research.google.com/drive/1ykfz3BqrtC_AUFegCzUQjjfUNlxp6Otc?usp=sharing) | [Code](https://colab.research.google.com/drive/12ZypxQh7OC6kBXvWZI-5d05I4m-B_hoR?usp=sharing) | [Code](https://colab.research.google.com/drive/1gdHyAx8XJsz2yNV-DHvbHjR1iCef5Qmh?usp=sharing) | [Code](https://colab.research.google.com/drive/1fm7wqx9MJ0kRrwKOfmLkK1Rmw-pySahB?usp=sharing) | |
-| seconds      | 1953  | 1982  | 1043  | 748   | 458   | 152   |
-| memory MB    | 40000 | 33217 | 27413 | 22161 |       | |
-| % saved|    | 16.96| 31.47 | 44.60 |       | | |
+## 💚 Community and Links
+| Type                                                                                                                                      | Links                                                                          |
+| ----------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------ |
+| <img width="16" src="https://cdn.prod.website-files.com/6257adef93867e50d84d30e2/66e3d80db9971f10a9757c99_Symbol.svg" />  **Discord**                       | [Join Discord server](https://discord.com/invite/unsloth)                          |
+| <img width="15" src="https://redditinc.com/hs-fs/hubfs/Reddit%20Inc/Brand/Reddit_Logo.png" />  **r/unsloth Reddit**                       | [Join Reddit community](https://reddit.com/r/unsloth)                          |
+| 📚 **Documentation & Wiki**                                                                                                               | [Read Our Docs](https://unsloth.ai/docs)                                       |
+| <img width="13" src="https://upload.wikimedia.org/wikipedia/commons/0/09/X_(formerly_Twitter)_logo_late_2025.svg" />  **Twitter (aka X)** | [Follow us on X](https://twitter.com/unslothai)                                |
+| 🔮 **Our Models**                                                                                                                         | [Unsloth Catalog](https://unsloth.ai/docs/get-started/unsloth-model-catalog)   |
+| ✍️ **Blog**                                                                                                                               | [Read our Blogs](https://unsloth.ai/blog)                                      |
 
-### 1 Tesla T4
+### Citation
 
-| 1 T4 16GB  | Hugging Face | Flash Attention | Unsloth Open    | Unsloth Pro Equal | Unsloth Pro   | Unsloth Max |
-|--------------|-------------|-----------------|-----------------|---------------|---------------|-------------|
-| Alpaca       | 1x          | 1.09x           | 1.69x           | 1.79x         | 2.93x          | **8.3x**        |
-| code | [Code](https://colab.research.google.com/drive/1XpLIV4s8Bj5uryB-X2gqM88oRGHEGdaB?usp=sharing) |    [Code](https://colab.research.google.com/drive/1LyXu6CjuymQg6ddHX8g1dpUvrMa1nn4L?usp=sharing) |    [Code](https://colab.research.google.com/drive/1gsv4LpY7C32otl1rgRo5wXTk4HIitXoM?usp=sharing) |    [Code](https://colab.research.google.com/drive/1VtULwRQwhEnVdNryjm27zXfdSM1tNfFK?usp=sharing) | | |
-| seconds       | 1599        | 1468        | 942             | 894          | 545           | 193         |
-| memory MB       | 7199        | 7059        | 6459            | 5443         |               |             |
-| % saved        |         | 1.94        | 10.28           | 24.39        |               | |
+You can cite the Unsloth repo as follows:
+```bibtex
+@software{unsloth,
+  author = {Daniel Han, Michael Han and Unsloth team},
+  title = {Unsloth},
+  url = {https://github.com/unslothai/unsloth},
+  year = {2023}
+}
+```
+If you trained a model with 🦥Unsloth, you can use this cool sticker!   <img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/made with unsloth.png" width="200" align="center" />
 
-| 1 T4 16GB  | Hugging Face | Flash Attention | Unsloth Open    | Unsloth Pro Equal | Unsloth Pro   | Unsloth Max |
-|--------------|-------------|-----------------|-----------------|---------------|---------------|-------------|
-| LAION Chip2  | 1x          | 0.99x           | 1.80x           | 1.75x         | 4.15x         | **11.75x**      |
-| code | [Code](https://colab.research.google.com/drive/1EtdStADehE4FVJnU2Cu6O8p9jDYdqG2L?usp=sharing) |    [Code](https://colab.research.google.com/drive/1Ik4jO68odUiQIJ_szZ3xok5fk58WpA5Q?usp=sharing) |    [Code](https://colab.research.google.com/drive/1E2nR4V3bXIWBQIUE7uR39lYPr3UikzqH?usp=sharing) |    [Code](https://colab.research.google.com/drive/13jbj8D8FOt9KyXwZt9Yf2MsYkD8CyCVR?usp=sharing) | | |
-| seconds  | 952         | 955         | 529             | 543          | 229           | 81          | 
-| memory MB  | 6037        | 6033        | 5797            | 4855         |               | |
-| % saved   |         | 0.07        | 3.98            | 19.58        |               | |
+### License
+Unsloth uses a dual-licensing model of Apache 2.0 and AGPL-3.0. The core Unsloth package remains licensed under **[Apache 2.0](https://github.com/unslothai/unsloth?tab=Apache-2.0-1-ov-file)**, while certain optional components, such as the Unsloth Studio UI are licensed under the open-source license **[AGPL-3.0](https://github.com/unslothai/unsloth?tab=AGPL-3.0-2-ov-file)**.
 
-| 1 T4 16GB  | Hugging Face | Flash Attention | Unsloth Open    | Unsloth Pro Equal | Unsloth Pro   | Unsloth Max |
-|--------------|-------------|-----------------|-----------------|---------------|---------------|-------------|
-| OASST        | 1x          | 1.19x           | 1.95x           | 1.86x         | 2.58x         | **7.3x**        |
-| code | [Code](https://colab.research.google.com/drive/1aXzGgEM3yYB6SWy_XR81nQFWME40ksSy?usp=sharing) |    [Code](https://colab.research.google.com/drive/1-5MdIOp0cM0scC-CdRZhh8OYhnGHqct4?usp=sharing) |    [Code](https://colab.research.google.com/drive/1n-fgduZhRUsSjgpqNtVkXA3rSfE7iBdg?usp=sharing) |    [Code](https://colab.research.google.com/drive/1z_GlHr2M_bB4lQrPhdWC7dseZv23cBIy?usp=sharing) | | |
-| seconds        | 2640        | 2222        | 1355            | 1421         | 1024          | 362         |
-| memory MB        | 14827       | 10391       | 8413            | 7031         |               | |
-| % saved         |         | 29.92       | 43.26           | 52.58        |               | |
+This structure helps support ongoing Unsloth development while keeping the project open source and enabling the broader ecosystem to continue growing.
 
-| 1 T4 16GB  | Hugging Face | Flash Attention | Unsloth Open    | Unsloth Pro Equal | Unsloth Pro   | Unsloth Max |
-|--------------|-------------|-----------------|-----------------|---------------|---------------|-------------|
-| Slim Orca    | 1x          | 1.21x           | 1.77x           | 1.85x         | 2.71x         | **7.67x**       |
-| code | [Code](https://colab.research.google.com/drive/15yLlJx9IE84kzx7ikky45pRcarPyUtEs?usp=sharing) |    [Code](https://colab.research.google.com/drive/16IShIBmjKULWy87I-xURpj4nztTkAF13?usp=sharing) |    [Code](https://colab.research.google.com/drive/1CJG3XLg_OQpCz71eB7Uqx7wuK_n2b-a8?usp=sharing) |    [Code](https://colab.research.google.com/drive/1UmwuWHtlrC6MAfl9mX7A_TRfo5iSHDa-?usp=sharing) | | |
-| seconds    | 2735        | 2262        | 1545            | 1478         | 1009          | 356         |
-| memory MB    | 13933       | 10489       | 7661            | 6563         |               | |
-| % saved    |         | 24.72       | 45.02           | 52.90        |               | |
-
-### 2 Tesla T4s via DDP
-
- | 2 T4 DDP | Hugging Face | Flash Attention | Unsloth Open | Unsloth Equal | Unsloth Pro | Unsloth Max |
-|--------------|----------|-------------|-----------------|--------------|---------------|-------------|
-| Alpaca       | 1x       | 0.99x       | 4.95x           | 4.44x        | 7.28x         | **20.61x**      |
-| code | [Code](https://www.kaggle.com/danielhanchen/hf-original-alpaca-t4-ddp) |   [Code](https://www.kaggle.com/danielhanchen/hf-sdpa-alpaca-t4-ddp) |   [Code](https://www.kaggle.com/danielhanchen/unsloth-alpaca-t4-ddp) | | |
-| seconds       | 9882     | 9946        | 1996            | 2227         | 1357          | 480         |
-| memory MB| 9176 | 9128 | 6904 | 6782 |  | |
-| % saved |     | 0.52 | 24.76 | 26.09 |  | | |
-
- | 2 T4 DDP | Hugging Face | Flash Attention | Unsloth Open | Unsloth Equal | Unsloth Pro | Unsloth Max |
-|--------------|----------|-------------|-----------------|--------------|---------------|-------------|
-| LAION Chip2  | 1x       | 1.12x       | 5.28x           | 4.21x        | 10.01x        | **28.32x**      |
-| code | [Code](https://www.kaggle.com/danielhanchen/hf-original-laion-t4-ddp) |    [Code](https://www.kaggle.com/danielhanchen/hf-sdpa-laion-t4-ddp) |    [Code](https://www.kaggle.com/danielhanchen/unsloth-laion-t4-ddp) | | |
-| seconds  | 5418     | 4854        | 1027            | 1286         | 541           | 191         |
-| memory MB| 7316 | 7316 | 5732 | 5934 |  | |
-| % saved |     | 0.00 | 21.65 | 18.89 |  | |
-
- | 2 T4 DDP | Hugging Face | Flash Attention | Unsloth Open | Unsloth Equal | Unsloth Pro | Unsloth Max |
-|--------------|----------|-------------|-----------------|--------------|---------------|-------------|
-| OASST (bsz=1)        | 1x       | 1.14x       | 5.56x           | 5.09x        | 5.64x         | **15.97x**      |
-| code | [Code](https://www.kaggle.com/danielhanchen/hf-original-oasst-bsz1-t4-ddp) |   [Code](https://www.kaggle.com/danielhanchen/hf-sdpa-oasst-bsz1-t4-ddp) |   [Code](https://www.kaggle.com/danielhanchen/unsloth-oasst-bsz1-t4-ddp) | | | |
-| seconds        | 4503 | 3955 | 811 | 885 | 798 | 282 |
-| memory MB | 11896 | 11628 | 6616 | 7105 |  | |
-| % saved |     | 2.25 | 44.38 | 40.27 |  | |
-
- | 2 T4 DDP | Hugging Face | Flash Attention | Unsloth Open | Unsloth Equal | Unsloth Pro | Unsloth Max |
-|--------------|----------|-------------|-----------------|--------------|---------------|-------------|
-| Slim Orca (bsz=1)    | 1x       | 0.97x       | 5.54x           | 4.68x        | 6.88x         | **19.46x**       |
-| code | [Code](https://www.kaggle.com/danielhanchen/hf-original-slimorca-bsz1-t4-ddp) |    [Code](https://www.kaggle.com/danielhanchen/hf-sdpa-slimorca-bsz1-t4-ddp) |    [Code](https://www.kaggle.com/danielhanchen/unsloth-slimorca-bsz1-t4-ddp) | | |
-| seconds | 4042 | 4158 | 729 | 863 | 588 | 208 |
-| memory MB| 11010 | 11042 | 6492 | 7410 |  | |
-| % saved |     | -0.29| 41.04 | 32.70 |  | | |
-
- | 2 T4 DDP | Hugging Face | Flash Attention | Unsloth Open | Unsloth Equal | Unsloth Pro | Unsloth Max |
-|--------------|----------|-------------|-----------------|--------------|---------------|-------------|
-| OASST (bsz=2)        | OOM ❌      | OOM ❌       |  ✓          | ✓         | ✓         | ✓ |
-| code | [Code](https://www.kaggle.com/danielhanchen/hf-original-oasst-t4-ddp) |    [Code](https://www.kaggle.com/danielhanchen/hf-sdpa-oasst-t4-ddp) |    [Code](https://www.kaggle.com/danielhanchen/unsloth-oasst-t4-ddp) | | | |
-| seconds        | OOM      | OOM         | 2719            | 3391         | 2794          | 987         |
-| memory MB| OOM  | OOM  | 8134 | 9600 |  | |
-| % saved | OOM  | OOM  |       |       |  | |
-
- | 2 T4 DDP | Hugging Face | Flash Attention | Unsloth Open | Unsloth Equal | Unsloth Pro | Unsloth Max |
-|--------------|----------|-------------|-----------------|--------------|---------------|-------------|
-| Slim Orca (bsz=2)    | OOM ❌       | OOM ❌       |  ✓          | ✓        | ✓         |✓ |
-| code  | [Code](https://www.kaggle.com/danielhanchen/hf-original-slimorca-t4-ddp) |     [Code](https://www.kaggle.com/danielhanchen/hf-sdpa-slimorca-t4-ddp) |     [Code](https://www.kaggle.com/danielhanchen/unsloth-slimorca-t4-ddp) | | |
-| seconds    | OOM      | OOM         | 2990            | 3444         | 2351          | 831         |
-| memory MB| OOM  | OOM  | 7594 | 8881 | | |
-| % saved | OOM  | OOM  |       |       |  | |
-
-# Credits
-1. [RandomInternetPreson](https://github.com/RandomInternetPreson) for confirming WSL support
-2. [152334H](https://github.com/152334H) for experimental DPO support
-3. [atgctg](https://github.com/atgctg) for syntax highlighting
-<img src="./images/unsloth loading page render.png" width="300" />
+### Thank You to
+- The [llama.cpp library](https://github.com/ggml-org/llama.cpp) that lets users run and save models with Unsloth
+- The Hugging Face team and their libraries: [transformers](https://github.com/huggingface/transformers) and [TRL](https://github.com/huggingface/trl)
+- The Pytorch and [Torch AO](https://github.com/unslothai/unsloth/pull/3391) team for their contributions
+- NVIDIA for their [NeMo DataDesigner](https://github.com/NVIDIA-NeMo/DataDesigner) library and their contributions
+- And of course for every single person who has contributed or has used Unsloth!

build.sh

@@ -0,0 +1,79 @@
+#!/usr/bin/env bash
+
+set -euo pipefail
+
+# 1. Build frontend (Vite outputs to dist/)
+cd studio/frontend
+
+# Clean stale dist to force a full rebuild
+rm -rf dist
+
+# Tailwind v4's oxide scanner respects .gitignore in parent directories.
+# Python venvs create a .gitignore with "*" (ignore everything), which
+# prevents Tailwind from scanning .tsx source files for class names.
+# Temporarily hide any such .gitignore during the build, then restore it.
+_HIDDEN_GITIGNORES=()
+_dir="$(pwd)"
+while [ "$_dir" != "/" ]; do
+    _dir="$(dirname "$_dir")"
+    if [ -f "$_dir/.gitignore" ] && grep -qx '\*' "$_dir/.gitignore" 2>/dev/null; then
+        mv "$_dir/.gitignore" "$_dir/.gitignore._twbuild"
+        _HIDDEN_GITIGNORES+=("$_dir/.gitignore")
+    fi
+done
+
+_restore_gitignores() {
+    for _gi in "${_HIDDEN_GITIGNORES[@]+"${_HIDDEN_GITIGNORES[@]}"}"; do
+        mv "${_gi}._twbuild" "$_gi" 2>/dev/null || true
+    done
+}
+trap _restore_gitignores EXIT
+
+# Use bun for install if available (faster), fall back to npm.
+_install_ok=false
+if command -v bun &>/dev/null; then
+    if bun install; then
+        _install_ok=true
+    else
+        echo "⚠ bun install failed, falling back to npm"
+        rm -rf node_modules
+    fi
+fi
+if [ "$_install_ok" != "true" ]; then
+    if ! npm install; then
+        echo "❌ ERROR: package install failed" >&2
+        exit 1
+    fi
+fi
+npm run build       # outputs to studio/frontend/dist/
+
+_restore_gitignores
+trap - EXIT
+
+# Validate CSS output -- catch truncated Tailwind builds before packaging
+MAX_CSS_SIZE=$(find dist/assets -name '*.css' -exec wc -c {} + 2>/dev/null | sort -n | tail -1 | awk '{print $1}')
+if [ -z "$MAX_CSS_SIZE" ]; then
+    echo "❌ ERROR: No CSS files were emitted into dist/assets."
+    echo "   The frontend build may have failed silently."
+    exit 1
+fi
+if [ "$MAX_CSS_SIZE" -lt 100000 ]; then
+    echo "❌ ERROR: Largest CSS file is only $((MAX_CSS_SIZE / 1024))KB (expected >100KB)."
+    echo "   Tailwind may not have scanned all source files."
+    echo "   Check for .gitignore files blocking the Tailwind oxide scanner."
+    exit 1
+fi
+echo "✅ Frontend CSS validated (${MAX_CSS_SIZE} bytes)"
+
+cd ../..
+
+# 2. Clean old artifacts
+rm -rf build dist *.egg-info
+
+# 3. Build wheel
+python -m build
+
+# 4. Optionally publish
+if [ "${1:-}" = "publish" ]; then
+    python -m twine upload dist/*
+fi

cli.py

@@ -0,0 +1,7 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+from unsloth_cli import app
+
+if __name__ == "__main__":
+    app()

scripts/enforce_kwargs_spacing.py

@@ -0,0 +1,179 @@
+#!/usr/bin/env python3
+"""Ensure keyword arguments use spaces around '=', prune redundant pass statements."""
+
+from __future__ import annotations
+
+import ast
+import argparse
+import io
+import sys
+import tokenize
+from collections import defaultdict
+from pathlib import Path
+
+
+def enforce_spacing(text: str) -> tuple[str, bool]:
+    """Return updated text with keyword '=' padded by spaces, plus change flag."""
+    lines = text.splitlines(keepends=True)
+    if not lines:
+        return text, False
+
+    offsets: dict[int, int] = defaultdict(int)
+    changed = False
+
+    reader = io.StringIO(text).readline
+    for token in tokenize.generate_tokens(reader):
+        if token.type != tokenize.OP or token.string != "=":
+            continue
+
+        line_index = token.start[0] - 1
+        col = token.start[1] + offsets[line_index]
+
+        if line_index < 0 or line_index >= len(lines):
+            continue
+
+        line = lines[line_index]
+        if col >= len(line) or line[col] != "=":
+            continue
+
+        line_changed = False
+
+        # Insert a space before '=' when missing and not preceded by whitespace.
+        if col > 0 and line[col - 1] not in {" ", "\t"}:
+            line = f"{line[:col]} {line[col:]}"
+            offsets[line_index] += 1
+            col += 1
+            line_changed = True
+            changed = True
+
+        # Insert a space after '=' when missing and not followed by whitespace or newline.
+        next_index = col + 1
+        if next_index < len(line) and line[next_index] not in {" ", "\t", "\n", "\r"}:
+            line = f"{line[:next_index]} {line[next_index:]}"
+            offsets[line_index] += 1
+            line_changed = True
+            changed = True
+
+        if line_changed:
+            lines[line_index] = line
+
+    if not changed:
+        return text, False
+
+    return "".join(lines), True
+
+
+def remove_redundant_passes(text: str) -> tuple[str, bool]:
+    """Drop pass statements that share a block with other executable code."""
+
+    try:
+        tree = ast.parse(text)
+    except SyntaxError:
+        return text, False
+
+    redundant: list[ast.Pass] = []
+
+    def visit(node: ast.AST) -> None:
+        for attr in ("body", "orelse", "finalbody"):
+            value = getattr(node, attr, None)
+            if not isinstance(value, list) or len(value) <= 1:
+                continue
+            for stmt in value:
+                if isinstance(stmt, ast.Pass):
+                    redundant.append(stmt)
+            for stmt in value:
+                if isinstance(stmt, ast.AST):
+                    visit(stmt)
+        handlers = getattr(node, "handlers", None)
+        if handlers:
+            for handler in handlers:
+                visit(handler)
+
+    visit(tree)
+
+    if not redundant:
+        return text, False
+
+    lines = text.splitlines(keepends=True)
+    changed = False
+
+    for node in sorted(
+        redundant, key=lambda item: (item.lineno, item.col_offset), reverse=True
+    ):
+        start = node.lineno - 1
+        end = (node.end_lineno or node.lineno) - 1
+        if start >= len(lines):
+            continue
+        changed = True
+        if start == end:
+            line = lines[start]
+            col_start = node.col_offset
+            col_end = node.end_col_offset or (col_start + 4)
+            segment = line[:col_start] + line[col_end:]
+            lines[start] = segment if segment.strip() else ""
+            continue
+
+        # Defensive fall-back for unexpected multi-line 'pass'.
+        prefix = lines[start][: node.col_offset]
+        lines[start] = prefix if prefix.strip() else ""
+        for idx in range(start + 1, end):
+            lines[idx] = ""
+        suffix = lines[end][(node.end_col_offset or 0) :]
+        lines[end] = suffix
+
+    # Normalise to ensure lines end with newlines except at EOF.
+    result_lines: list[str] = []
+    for index, line in enumerate(lines):
+        if not line:
+            continue
+        if index < len(lines) - 1 and not line.endswith("\n"):
+            result_lines.append(f"{line}\n")
+        else:
+            result_lines.append(line)
+
+    return "".join(result_lines), changed
+
+
+def process_file(path: Path) -> bool:
+    try:
+        with tokenize.open(path) as handle:
+            original = handle.read()
+            encoding = handle.encoding
+    except (OSError, SyntaxError) as exc:  # SyntaxError from tokenize on invalid python
+        print(f"Failed to read {path}: {exc}", file=sys.stderr)
+        return False
+
+    updated, changed = enforce_spacing(original)
+    updated, removed = remove_redundant_passes(updated)
+    if changed or removed:
+        path.write_text(updated, encoding=encoding)
+        return True
+    return False
+
+
+def main(argv: list[str]) -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("files", nargs="+", help="Python files to fix")
+    args = parser.parse_args(argv)
+
+    touched: list[Path] = []
+    self_path = Path(__file__).resolve()
+
+    for entry in args.files:
+        path = Path(entry)
+        # Skip modifying this script to avoid self-edit loops.
+        if path.resolve() == self_path:
+            continue
+        if not path.exists() or path.is_dir():
+            continue
+        if process_file(path):
+            touched.append(path)
+
+    if touched:
+        for path in touched:
+            print(f"Adjusted kwarg spacing in {path}")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main(sys.argv[1:]))

scripts/install_gemma4_mlx.sh

@@ -0,0 +1,169 @@
+#!/bin/bash
+set -e
+
+# ============================================================
+# Gemma 4 MLX — One-command setup + inference
+#
+# Usage:
+#   bash install_gemma4_mlx.sh [--venv-dir DIR]
+#
+# This script:
+#   1. Creates a Python virtual environment
+#   2. Installs uv, mlx-vlm, transformers
+# ============================================================
+
+# ── Output style (inspired by unsloth/install.sh) ─────────────
+RULE=""
+_rule_i=0
+while [ "$_rule_i" -lt 52 ]; do
+    RULE="${RULE}─"
+    _rule_i=$((_rule_i + 1))
+done
+
+if [ -n "${NO_COLOR:-}" ]; then
+    C_TITLE= C_DIM= C_OK= C_WARN= C_ERR= C_RST=
+elif [ -t 1 ] || [ -n "${FORCE_COLOR:-}" ]; then
+    _ESC="$(printf '\033')"
+    C_TITLE="${_ESC}[38;5;117m"
+    C_DIM="${_ESC}[38;5;245m"
+    C_OK="${_ESC}[38;5;108m"
+    C_WARN="${_ESC}[38;5;136m"
+    C_ERR="${_ESC}[91m"
+    C_RST="${_ESC}[0m"
+else
+    C_TITLE= C_DIM= C_OK= C_WARN= C_ERR= C_RST=
+fi
+
+step()    { printf "  ${C_DIM}%-18.18s${C_RST}${3:-$C_OK}%s${C_RST}\n" "$1" "$2"; }
+substep() { printf "  ${C_DIM}%-18s${2:-$C_DIM}%s${C_RST}\n" "" "$1"; }
+fail()    { step "error" "$1" "$C_ERR"; exit 1; }
+
+# ── Parse flags ───────────────────────────────────────────────
+VENV_DIR=""
+_next_is_venv=false
+
+for arg in "$@"; do
+    if [ "$_next_is_venv" = true ]; then
+        VENV_DIR="$arg"
+        _next_is_venv=false
+        continue
+    fi
+    case "$arg" in
+        --venv-dir)  _next_is_venv=true ;;
+    esac
+done
+
+# Default venv location
+if [ -z "$VENV_DIR" ]; then
+    VENV_DIR="$HOME/.unsloth/unsloth_gemma4_mlx"
+fi
+
+# ── Banner ────────────────────────────────────────────────────
+echo ""
+printf "  ${C_TITLE}%s${C_RST}\n" "💎 Gemma 4 MLX Installer"
+printf "  ${C_DIM}%s${C_RST}\n" "$RULE"
+echo ""
+
+# ── Platform check ────────────────────────────────────────────
+if [ "$(uname)" != "Darwin" ]; then
+    fail "MLX requires macOS with Apple Silicon. Detected: $(uname)"
+fi
+
+_ARCH=$(uname -m)
+if [ "$_ARCH" != "arm64" ]; then
+    step "warning" "Apple Silicon recommended (detected: $_ARCH)" "$C_WARN"
+fi
+
+step "platform" "macOS ($_ARCH)"
+
+# ── Detect Python ─────────────────────────────────────────────
+PYTHON=""
+for _candidate in python3.12 python3.11 python3.13 python3; do
+    if command -v "$_candidate" >/dev/null 2>&1; then
+        PYTHON="$_candidate"
+        break
+    fi
+done
+
+if [ -z "$PYTHON" ]; then
+    fail "Python 3 not found. Install via: brew install python@3.12"
+fi
+
+_PY_VERSION=$("$PYTHON" -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}')")
+step "python" "$PYTHON ($_PY_VERSION)"
+
+# ── Create virtual environment ────────────────────────────────
+if [ -x "$VENV_DIR/bin/python" ]; then
+    step "venv" "using existing environment"
+    substep "$VENV_DIR"
+else
+    step "venv" "creating virtual environment"
+    substep "$VENV_DIR"
+    mkdir -p "$(dirname "$VENV_DIR")"
+    "$PYTHON" -m venv "$VENV_DIR"
+fi
+
+# ── Install uv ───────────────────────────────────────────────
+if ! command -v uv >/dev/null 2>&1; then
+    step "uv" "installing uv package manager..."
+    _uv_tmp=$(mktemp)
+    curl -LsSf "https://astral.sh/uv/install.sh" -o "$_uv_tmp"
+    sh "$_uv_tmp" </dev/null >/dev/null 2>&1
+    rm -f "$_uv_tmp"
+    if [ -f "$HOME/.local/bin/env" ]; then
+        . "$HOME/.local/bin/env"
+    fi
+    export PATH="$HOME/.local/bin:$PATH"
+    substep "done"
+else
+    step "uv" "found $(uv --version 2>/dev/null || echo 'uv')"
+fi
+
+_VENV_PY="$VENV_DIR/bin/python"
+
+# ── Install dependencies ──────────────────────────────────────
+step "install" "installing mlx-vlm..."
+uv pip install --python "$_VENV_PY" -q mlx-vlm
+substep "done"
+
+step "install" "installing transformers>=5.5.0..."
+if uv pip install --python "$_VENV_PY" -q "transformers>=5.5.0" 2>/dev/null; then
+    substep "installed from PyPI"
+else
+    substep "PyPI install failed (Python <3.10?), trying GitHub..."
+    if uv pip install --python "$_VENV_PY" -q "git+https://github.com/huggingface/transformers.git@v5.5-release" 2>/dev/null; then
+        substep "installed from huggingface/transformers v5.5-release"
+    else
+        step "warning" "could not install transformers>=5.5.0" "$C_WARN"
+        substep "tried: PyPI, huggingface/transformers v5.5-release"
+    fi
+fi
+
+# ── Verify installation ──────────────────────────────────────
+if "$_VENV_PY" -c "import mlx_vlm"; then
+    substep "mlx-vlm verified"
+else
+    fail "Installation verification failed."
+fi
+
+# ── Done ──────────────────────────────────────────────────────
+echo ""
+printf "  ${C_TITLE}%s${C_RST}\n" "Gemma 4 MLX installed!"
+printf "  ${C_DIM}%s${C_RST}\n" "$RULE"
+echo ""
+step "available models" "unsloth/gemma-4-E2B-it-UD-MLX-4bit"
+substep "unsloth/gemma-4-E4B-it-UD-MLX-4bit"
+substep "unsloth/gemma-4-26b-a4b-it-UD-MLX-4bit"
+substep "unsloth/gemma-4-31b-it-UD-MLX-4bit"
+echo ""
+step "venv activate" "source ${VENV_DIR}/bin/activate"
+echo ""
+step "text chat" "python -m mlx_vlm.chat --model unsloth/gemma-4-E2B-it-UD-MLX-4bit"
+echo ""
+step "vision chat" "python -m mlx_vlm.chat --model unsloth/gemma-4-31b-it-UD-MLX-4bit"
+substep "Use /image path/to/image.jpg to load an image"
+echo ""
+step "gradio UI" "python -m mlx_vlm.chat_ui --model unsloth/gemma-4-31b-it-UD-MLX-4bit"
+echo ""
+printf "  ${C_DIM}%s${C_RST}\n" "$RULE"
+echo ""

scripts/install_qwen3_6_mlx.sh

@@ -0,0 +1,191 @@
+#!/bin/bash
+set -e
+
+# ============================================================
+# Qwen3.6 MLX — One-command setup + inference
+#
+# Usage:
+#   bash install_qwen3_6_mlx.sh [--venv-dir DIR]
+#
+# This script:
+#   1. Creates a Python virtual environment
+#   2. Installs uv, mlx-vlm, transformers, torch, torchvision
+# ============================================================
+
+# ── Output style (inspired by unsloth/install.sh) ─────────────
+RULE=""
+_rule_i=0
+while [ "$_rule_i" -lt 52 ]; do
+    RULE="${RULE}─"
+    _rule_i=$((_rule_i + 1))
+done
+
+if [ -n "${NO_COLOR:-}" ]; then
+    C_TITLE= C_DIM= C_OK= C_WARN= C_ERR= C_RST=
+elif [ -t 1 ] || [ -n "${FORCE_COLOR:-}" ]; then
+    _ESC="$(printf '\033')"
+    C_TITLE="${_ESC}[38;5;117m"
+    C_DIM="${_ESC}[38;5;245m"
+    C_OK="${_ESC}[38;5;108m"
+    C_WARN="${_ESC}[38;5;136m"
+    C_ERR="${_ESC}[91m"
+    C_RST="${_ESC}[0m"
+else
+    C_TITLE= C_DIM= C_OK= C_WARN= C_ERR= C_RST=
+fi
+
+step()    { printf "  ${C_DIM}%-18.18s${C_RST}${3:-$C_OK}%s${C_RST}\n" "$1" "$2"; }
+substep() { printf "  ${C_DIM}%-18s${2:-$C_DIM}%s${C_RST}\n" "" "$1"; }
+fail()    { step "error" "$1" "$C_ERR"; exit 1; }
+
+# ── Parse flags ───────────────────────────────────────────────
+VENV_DIR=""
+_next_is_venv=false
+
+for arg in "$@"; do
+    if [ "$_next_is_venv" = true ]; then
+        VENV_DIR="$arg"
+        _next_is_venv=false
+        continue
+    fi
+    case "$arg" in
+        --venv-dir)  _next_is_venv=true ;;
+    esac
+done
+
+# Default venv location
+if [ -z "$VENV_DIR" ]; then
+    VENV_DIR="$HOME/.unsloth/unsloth_qwen3_6_mlx"
+fi
+
+# ── Banner ────────────────────────────────────────────────────
+echo ""
+printf "  ${C_TITLE}%s${C_RST}\n" "Qwen3.6 MLX Installer"
+printf "  ${C_DIM}%s${C_RST}\n" "$RULE"
+echo ""
+
+# ── Platform check ────────────────────────────────────────────
+if [ "$(uname)" != "Darwin" ]; then
+    fail "MLX requires macOS with Apple Silicon. Detected: $(uname)"
+fi
+
+_ARCH=$(uname -m)
+if [ "$_ARCH" != "arm64" ]; then
+    step "warning" "Apple Silicon recommended (detected: $_ARCH)" "$C_WARN"
+fi
+
+step "platform" "macOS ($_ARCH)"
+
+# ── Detect Python ─────────────────────────────────────────────
+PYTHON=""
+for _candidate in python3.12 python3.11 python3.13 python3; do
+    if command -v "$_candidate" >/dev/null 2>&1; then
+        PYTHON="$_candidate"
+        break
+    fi
+done
+
+if [ -z "$PYTHON" ]; then
+    fail "Python 3 not found. Install via: brew install python@3.12"
+fi
+
+_PY_VERSION=$("$PYTHON" -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}')")
+step "python" "$PYTHON ($_PY_VERSION)"
+
+# ── Create virtual environment ────────────────────────────────
+if [ -x "$VENV_DIR/bin/python" ]; then
+    step "venv" "using existing environment"
+    substep "$VENV_DIR"
+else
+    step "venv" "creating virtual environment"
+    substep "$VENV_DIR"
+    mkdir -p "$(dirname "$VENV_DIR")"
+    "$PYTHON" -m venv "$VENV_DIR"
+fi
+
+# ── Install uv ───────────────────────────────────────────────
+if ! command -v uv >/dev/null 2>&1; then
+    step "uv" "installing uv package manager..."
+    _uv_tmp=$(mktemp)
+    curl -LsSf "https://astral.sh/uv/install.sh" -o "$_uv_tmp"
+    sh "$_uv_tmp" </dev/null
+    rm -f "$_uv_tmp"
+    if [ -f "$HOME/.local/bin/env" ]; then
+        . "$HOME/.local/bin/env"
+    fi
+    export PATH="$HOME/.local/bin:$PATH"
+    substep "done"
+else
+    step "uv" "found $(uv --version 2>/dev/null || echo 'uv')"
+fi
+
+_VENV_PY="$VENV_DIR/bin/python"
+
+# ── Install dependencies ──────────────────────────────────────
+step "install" "installing mlx-vlm..."
+uv pip install --python "$_VENV_PY" -q mlx-vlm
+substep "done"
+
+step "install" "installing transformers>=5.2.0..."
+if uv pip install --python "$_VENV_PY" -q "transformers>=5.2.0"; then
+    substep "installed from PyPI"
+else
+    substep "PyPI install failed, trying GitHub..."
+    if uv pip install --python "$_VENV_PY" -q "git+https://github.com/huggingface/transformers.git"; then
+        substep "installed from huggingface/transformers main"
+    else
+        fail "Could not install transformers>=5.2.0 (required for Qwen3.5/3.6 model support). Please check your Python version (>=3.10 required) and network connection, then try again."
+    fi
+fi
+
+step "install" "installing torch + torchvision (needed for Qwen3 VL processor)..."
+uv pip install --python "$_VENV_PY" -q torch torchvision
+substep "done"
+
+# ── Verify installation ──────────────────────────────────────
+if "$_VENV_PY" -c "import mlx_vlm; import torch; import torchvision; import transformers"; then
+    substep "mlx-vlm + torch + transformers verified"
+else
+    fail "Installation verification failed. Please ensure Python >=3.10 and try again."
+fi
+
+# ── Apply patches for multi-turn image chat ──────────────────
+_PATCH_BASE="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/fix/ui-fix/unsloth/models/patches/mlx_vlm_qwen3_5"
+_SITE_PKGS=$("$_VENV_PY" -c "import site; print(site.getsitepackages()[0])")
+
+step "patch" "fixing multi-turn image chat..."
+
+if curl -sSLf "${_PATCH_BASE}/qwen3_5.py" -o "${_SITE_PKGS}/mlx_vlm/models/qwen3_5/qwen3_5.py"; then
+    substep "patched qwen3_5.py (MRoPE position reset)"
+else
+    step "warning" "failed to download qwen3_5.py patch — multi-turn image chat may not work" "$C_WARN"
+fi
+
+if curl -sSLf "${_PATCH_BASE}/generate.py" -o "${_SITE_PKGS}/mlx_vlm/generate.py"; then
+    substep "patched generate.py (mask trim on cache reuse)"
+else
+    step "warning" "failed to download generate.py patch — multi-turn image chat may not work" "$C_WARN"
+fi
+
+# Clear pycache so patches take effect
+find "${_SITE_PKGS}/mlx_vlm" -name "__pycache__" -type d -exec rm -rf {} + 2>/dev/null || true
+substep "cleared bytecode cache"
+
+# ── Done ──────────────────────────────────────────────────────
+echo ""
+printf "  ${C_TITLE}%s${C_RST}\n" "Qwen3.6 MLX installed!"
+printf "  ${C_DIM}%s${C_RST}\n" "$RULE"
+echo ""
+step "available models" "unsloth/Qwen3.6-35B-A3B-UD-MLX-3bit"
+substep "unsloth/Qwen3.6-35B-A3B-UD-MLX-4bit"
+substep "unsloth/Qwen3.6-35B-A3B-MLX-8bit"
+echo ""
+step "venv activate" "source ${VENV_DIR}/bin/activate"
+echo ""
+step "vision chat" "python -m mlx_vlm.chat --model unsloth/Qwen3.6-35B-A3B-UD-MLX-4bit"
+substep "Use /image path/to/image.jpg to load an image"
+echo ""
+step "gradio UI" "python -m mlx_vlm.chat_ui --model unsloth/Qwen3.6-35B-A3B-UD-MLX-4bit"
+echo ""
+printf "  ${C_DIM}%s${C_RST}\n" "$RULE"
+echo ""

scripts/run_ruff_format.py

@@ -0,0 +1,30 @@
+#!/usr/bin/env python3
+"""Run `ruff format` followed by kwarg spacing enforcement."""
+
+from __future__ import annotations
+
+import subprocess
+import sys
+from pathlib import Path
+
+HERE = Path(__file__).resolve().parent
+
+
+def main(argv: list[str]) -> int:
+    files = [arg for arg in argv if Path(arg).exists()]
+    if not files:
+        return 0
+
+    ruff_cmd = [sys.executable, "-m", "ruff", "format", *files]
+    ruff_proc = subprocess.run(ruff_cmd)
+    if ruff_proc.returncode != 0:
+        return ruff_proc.returncode
+
+    spacing_script = HERE / "enforce_kwargs_spacing.py"
+    spacing_cmd = [sys.executable, str(spacing_script), *files]
+    spacing_proc = subprocess.run(spacing_cmd)
+    return spacing_proc.returncode
+
+
+if __name__ == "__main__":
+    raise SystemExit(main(sys.argv[1:]))

studio/LICENSE.AGPL-3.0

@@ -0,0 +1,661 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
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+
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+
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+have the freedom to distribute copies of free software (and charge for
+them if you wish), that you receive source code or can get it if you
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+
+  Developers that use our General Public Licenses protect your rights
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+
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+
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+<https://www.gnu.org/licenses/>.

studio/Unsloth_Studio_Colab.ipynb

@@ -0,0 +1,153 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "view-in-github",
+    "colab_type": "text"
+   },
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b87de59",
+   "metadata": {
+    "id": "6b87de59"
+   },
+   "source": [
+    "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n",
+    "<div class=\"align-center\">\n",
+    "<a href=\"https://unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
+    "<a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord button.png\" width=\"145\"></a>\n",
+    "<a href=\"https://unsloth.ai/docs/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a> Join Discord if you need help + ⭐ <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> ⭐\n",
+    "</div>\n",
+    "\n",
+    "To install Unsloth Studio on your local device, follow [our guide](https://unsloth.ai/docs/new/unsloth-studio/install). Unsloth Studio is licensed [AGPL-3.0](https://github.com/unslothai/unsloth/blob/main/studio/LICENSE.AGPL-3.0).\n",
+    "\n",
+    "### Unsloth Studio\n",
+    "\n",
+    "Train and run open models with [**Unsloth Studio**](https://unsloth.ai/docs/new/unsloth-studio/start). NEW! Installation should now only take 2 mins!\n",
+    "\n",
+    "\n",
+    "We are actively working on making Unsloth Studio install on Colab T4 GPUs faster.\n",
+    "\n",
+    "[Features](https://unsloth.ai/docs/new/unsloth-studio#features) • [Quickstart](https://unsloth.ai/docs/new/unsloth-studio/start) • [Data Recipes](https://unsloth.ai/docs/new/unsloth-studio/data-recipe) • [Studio Chat](https://unsloth.ai/docs/new/unsloth-studio/chat) • [Export](https://unsloth.ai/docs/new/unsloth-studio/export)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4206349",
+   "metadata": {
+    "id": "e4206349"
+   },
+   "source": [
+    "<p align=\"left\"><img src=\"https://github.com/unslothai/unsloth/raw/main/studio/frontend/public/studio%20github%20landscape%20colab%20display.png\" width=\"600\"></p>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27da2957",
+   "metadata": {
+    "id": "27da2957"
+   },
+   "source": [
+    "### Setup: Clone repo and run setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27e68f91",
+   "metadata": {
+    "id": "27e68f91"
+   },
+   "outputs": [],
+   "source": "!git clone --depth 1 --branch main https://github.com/unslothai/unsloth.git\n%cd /content/unsloth\n!chmod +x studio/setup.sh && ./studio/setup.sh"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e1771a9",
+   "metadata": {
+    "id": "3e1771a9"
+   },
+   "source": [
+    "### Start Unsloth Studio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "277e431e",
+   "metadata": {
+    "id": "277e431e"
+   },
+   "outputs": [],
+   "source": [
+    "import sys, time\n",
+    "sys.path.insert(0, \"/content/unsloth/studio/backend\")\n",
+    "from colab import start\n",
+    "start()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "from google.colab import output\n",
+    "output.serve_kernel_port_as_iframe(8888, height = 1200, width = \"100%\")\n",
+    "for _ in range(10000): time.sleep(300), print(\"=\", end = \"\")"
+   ],
+   "metadata": {
+    "id": "wb9UELh--XzX"
+   },
+   "id": "wb9UELh--XzX",
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2b0c6a1",
+   "metadata": {
+    "id": "f2b0c6a1"
+   },
+   "source": [
+    "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n",
+    "\n",
+    "Some other resources:\n",
+    "1. Looking to use Unsloth locally? Read our [Installation Guide](https://unsloth.ai/docs/get-started/install) for details on installing Unsloth on Windows, Docker, AMD, Intel GPUs.\n",
+    "2. Learn how to do Reinforcement Learning with our [RL Guide and notebooks](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide).\n",
+    "3. Read our guides and notebooks for [Text-to-speech (TTS)](https://unsloth.ai/docs/basics/text-to-speech-tts-fine-tuning) and [vision](https://unsloth.ai/docs/basics/vision-fine-tuning) model support.\n",
+    "4. Explore our [LLM Tutorials Directory](https://unsloth.ai/docs/models/tutorials-how-to-fine-tune-and-run-llms) to find dedicated guides for each model.\n",
+    "5. Need help with Inference? Read our [Inference & Deployment page](https://unsloth.ai/docs/basics/inference-and-deployment) for details on using vLLM, llama.cpp, Ollama etc.\n",
+    "\n",
+    "<div class=\"align-center\">\n",
+    "  <a href=\"https://unsloth.ai\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
+    "  <a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord.png\" width=\"145\"></a>\n",
+    "  <a href=\"https://unsloth.ai/docs/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a>\n",
+    "\n",
+    "  Join Discord if you need help + ⭐️ <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> ⭐️\n",
+    "\n",
+    "  <b>This notebook is licensed <a href=\"https://github.com/unslothai/unsloth/blob/main/studio/LICENSE.AGPL-3.0\">AGPL-3.0</a></b>\n",
+    "</div>"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": [],
+   "include_colab_link": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

studio/__init__.py

@@ -0,0 +1,2 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0

studio/backend/__init__.py

@@ -0,0 +1,2 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0

studio/backend/_platform_compat.py

@@ -0,0 +1,59 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
+
+"""
+Compatibility shim for Anaconda/conda-forge Python builds.
+
+Anaconda modifies sys.version to include distributor metadata between pipe
+characters, e.g. '3.12.4 | packaged by Anaconda, Inc. | (main, ...) [MSC ...]'.
+Python's platform._sys_version() has a hardcoded regex that cannot parse this,
+raising ValueError. CPython closed this as "not planned" (cpython#102396).
+
+This module seeds platform._sys_version_cache so the stdlib parser never sees
+the problematic string, fixing the import chain:
+    structlog -> rich.pretty -> attrs._compat -> platform.python_implementation()
+
+Import this module before any library imports that may trigger the above chain.
+Safe to import multiple times (no-op if cache is already seeded or no pipes).
+"""
+
+import platform
+import re
+import sys
+
+
+def _seed_sys_version_cache() -> None:
+    """One-shot cache prime: parse a cleaned sys.version and seed the cache."""
+    raw = sys.version
+
+    # Strip paired |...| segments (Anaconda, conda-forge metadata)
+    cleaned = re.sub(r"\s*\|[^|]*\|\s*", " ", raw).strip()
+
+    # Format B: "ver (build) | label | (build_dup) \n[compiler]"
+    # After pipe-strip, two consecutive (...) groups remain; drop the second.
+    cleaned = re.sub(r"(\([^)]*\))\s+\([^)]*\)", r"\1", cleaned)
+
+    if "|" in cleaned:
+        # Unpaired pipe remaining -- keep version + everything from "(" onward
+        m = re.match(r"([\w.+]+)\s*", cleaned)
+        p = cleaned.find("(")
+        if m and p > 0:
+            cleaned = m.group(0) + cleaned[p:]
+
+    if cleaned == raw:
+        return  # Nothing to fix
+
+    # Parse the cleaned string through the real stdlib parser
+    try:
+        result = platform._sys_version(cleaned)
+    except ValueError:
+        return  # Cleaning didn't produce a parseable string; don't make things worse
+
+    # Seed the cache so future calls with the raw string skip parsing entirely
+    cache = getattr(platform, "_sys_version_cache", None)
+    if isinstance(cache, dict):
+        cache[raw] = result
+
+
+if "|" in sys.version:
+    _seed_sys_version_cache()

studio/backend/assets/__init__.py

@@ -0,0 +1,2 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0

studio/backend/assets/configs/__init__.py

@@ -0,0 +1,2 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0

studio/backend/assets/configs/full_finetune.yaml

@@ -0,0 +1,42 @@
+model: unsloth/Qwen2.5-0.5B
+
+data:
+  dataset: tatsu-lab/alpaca
+  format_type: auto
+
+training:
+  training_type: full
+  max_seq_length: 2048
+  load_in_4bit: false
+  output_dir: outputs
+  num_epochs: 1
+  learning_rate: 2e-5
+  batch_size: 1
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 0
+  save_steps: 0
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: false
+  gradient_checkpointing: "unsloth"
+
+lora:
+  lora_r: 64
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules: ""
+  vision_all_linear: false
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: unsloth-training
+  enable_tensorboard: false
+  tensorboard_dir: runs

studio/backend/assets/configs/inference_defaults.json

@@ -0,0 +1,398 @@
+{
+  "_comment": "Per-model-family inference parameter defaults. Sources: (1) Ollama params blobs, (2) Existing Unsloth Studio YAML configs. Patterns ordered longest-match-first.",
+  "families": {
+    "qwen3.6": {
+      "temperature": 0.7,
+      "top_p": 0.8,
+      "top_k": 20,
+      "min_p": 0.0,
+      "repetition_penalty": 1.0,
+      "presence_penalty": 1.5
+    },
+    "qwen3.5": {
+      "temperature": 0.7,
+      "top_p": 0.8,
+      "top_k": 20,
+      "min_p": 0.0,
+      "repetition_penalty": 1.0,
+      "presence_penalty": 1.5
+    },
+    "qwen3-coder": {
+      "temperature": 0.7,
+      "top_p": 0.8,
+      "top_k": 20,
+      "min_p": 0.0,
+      "repetition_penalty": 1.0
+    },
+    "qwen3-next": {
+      "temperature": 0.7,
+      "top_p": 0.8,
+      "top_k": 20,
+      "min_p": 0.0,
+      "repetition_penalty": 1.0
+    },
+    "qwen3-vl": {
+      "temperature": 0.7,
+      "top_p": 0.8,
+      "top_k": 20,
+      "min_p": 0.0,
+      "repetition_penalty": 1.0
+    },
+    "qwen3": {
+      "temperature": 0.6,
+      "top_p": 0.95,
+      "top_k": 20,
+      "min_p": 0.0,
+      "repetition_penalty": 1.0
+    },
+    "qwen2.5-coder": {
+      "temperature": 1.5,
+      "top_p": 0.95,
+      "top_k": -1,
+      "min_p": 0.1,
+      "repetition_penalty": 1.0
+    },
+    "qwen2.5-vl": {
+      "temperature": 1.5,
+      "top_p": 0.95,
+      "top_k": -1,
+      "min_p": 0.1,
+      "repetition_penalty": 1.0
+    },
+    "qwen2.5-omni": {
+      "temperature": 0.7,
+      "top_p": 0.8,
+      "top_k": 20,
+      "min_p": 0.0,
+      "repetition_penalty": 1.0
+    },
+    "qwen2.5-math": {
+      "temperature": 0.7,
+      "top_p": 0.8,
+      "top_k": 20,
+      "min_p": 0.0,
+      "repetition_penalty": 1.0
+    },
+    "qwen2.5": {
+      "temperature": 0.7,
+      "top_p": 0.8,
+      "top_k": 20,
+      "min_p": 0.0,
+      "repetition_penalty": 1.0
+    },
+    "qwen2-vl": {
+      "temperature": 1.5,
+      "top_p": 0.95,
+      "top_k": -1,
+      "min_p": 0.1,
+      "repetition_penalty": 1.0
+    },
+    "qwen2": {
+      "temperature": 0.7,
+      "top_p": 0.8,
+      "top_k": 20,
+      "min_p": 0.0,
+      "repetition_penalty": 1.0
+    },
+    "qwq": {
+      "temperature": 0.6,
+      "top_p": 0.95,
+      "top_k": 40,
+      "min_p": 0.0,
+      "repetition_penalty": 1.0
+    },
+    "gemma-4": {
+      "temperature": 1.0,
+      "top_p": 0.95,
+      "top_k": 64,
+      "min_p": 0.0,
+      "repetition_penalty": 1.0,
+      "presence_penalty": 0.0
+    },
+    "gemma-3n": {
+      "temperature": 1.0,
+      "top_p": 0.95,
+      "top_k": 64,
+      "min_p": 0.0,
+      "repetition_penalty": 1.0
+    },
+    "gemma-3": {
+      "temperature": 1.0,
+      "top_p": 0.95,
+      "top_k": 64,
+      "min_p": 0.0,
+      "repetition_penalty": 1.0
+    },
+    "medgemma": {
+      "temperature": 1.0,
+      "top_p": 0.95,
+      "top_k": 64,
+      "min_p": 0.0,
+      "repetition_penalty": 1.0
+    },
+    "gemma-2": {
+      "temperature": 1.0,
+      "top_p": 0.95,
+      "top_k": 64,
+      "min_p": 0.0,
+      "repetition_penalty": 1.0
+    },
+    "llama-4": {
+      "temperature": 1.0,
+      "top_p": 0.9,
+      "top_k": -1,
+      "min_p": 0.01,
+      "repetition_penalty": 1.0
+    },
+    "llama-3.3": {
+      "temperature": 1.5,
+      "top_p": 0.95,
+      "top_k": -1,
+      "min_p": 0.1,
+      "repetition_penalty": 1.0
+    },
+    "llama-3.2": {
+      "temperature": 1.5,
+      "top_p": 0.95,
+      "top_k": -1,
+      "min_p": 0.1,
+      "repetition_penalty": 1.0
+    },
+    "llama-3.1": {
+      "temperature": 1.5,
+      "top_p": 0.95,
+      "top_k": -1,
+      "min_p": 0.1,
+      "repetition_penalty": 1.0
+    },
+    "llama-3": {
+      "temperature": 1.5,
+      "top_p": 0.95,
+      "top_k": -1,
+      "min_p": 0.1,
+      "repetition_penalty": 1.0
+    },
+    "phi-4": {
+      "temperature": 0.8,
+      "top_p": 0.95,
+      "top_k": -1,
+      "min_p": 0.0,
+      "repetition_penalty": 1.0
+    },
+    "phi-3": {
+      "temperature": 0.7,
+      "top_p": 0.9,
+      "top_k": -1,
+      "min_p": 0.01,
+      "repetition_penalty": 1.0
+    },
+    "mistral-nemo": {
+      "temperature": 0.7,
+      "top_p": 0.95,
+      "top_k": -1,
+      "min_p": 0.01,
+      "repetition_penalty": 1.0
+    },
+    "mistral-small": {
+      "temperature": 0.15,
+      "top_p": 0.95,
+      "top_k": -1,
+      "min_p": 0.01,
+      "repetition_penalty": 1.0
+    },
+    "mistral-large": {
+      "temperature": 0.7,
+      "top_p": 0.95,
+      "top_k": -1,
+      "min_p": 0.01,
+      "repetition_penalty": 1.0
+    },
+    "magistral": {
+      "temperature": 0.7,
+      "top_p": 0.95,
+      "top_k": -1,
+      "min_p": 0.01,
+      "repetition_penalty": 1.0
+    },
+    "ministral": {
+      "temperature": 0.15,
+      "top_p": 0.95,
+      "top_k": -1,
+      "min_p": 0.01,
+      "repetition_penalty": 1.0
+    },
+    "devstral": {
+      "temperature": 0.7,
+      "top_p": 0.95,
+      "top_k": -1,
+      "min_p": 0.01,
+      "repetition_penalty": 1.0
+    },
+    "pixtral": {
+      "temperature": 1.5,
+      "top_p": 0.95,
+      "top_k": -1,
+      "min_p": 0.1,
+      "repetition_penalty": 1.0
+    },
+    "deepseek-r1": {
+      "temperature": 0.6,
+      "top_p": 0.95,
+      "top_k": -1,
+      "min_p": 0.01,
+      "repetition_penalty": 1.0
+    },
+    "deepseek-v3": {
+      "temperature": 0.6,
+      "top_p": 0.95,
+      "top_k": -1,
+      "min_p": 0.01,
+      "repetition_penalty": 1.0
+    },
+    "deepseek-ocr": {
+      "temperature": 0.0,
+      "top_p": 0.95,
+      "top_k": -1,
+      "min_p": 0.01,
+      "repetition_penalty": 1.0
+    },
+    "glm-5": {
+      "temperature": 1.0,
+      "top_p": 0.95,
+      "top_k": -1,
+      "min_p": 0.01,
+      "repetition_penalty": 1.0
+    },
+    "glm-4": {
+      "temperature": 1.0,
+      "top_p": 0.95,
+      "top_k": -1,
+      "min_p": 0.01,
+      "repetition_penalty": 1.0
+    },
+    "nemotron": {
+      "temperature": 1.0,
+      "top_p": 1.0,
+      "top_k": -1,
+      "min_p": 0.01,
+      "repetition_penalty": 1.0
+    },
+    "minimax-m2.5": {
+      "temperature": 1.0,
+      "top_p": 0.95,
+      "top_k": 40,
+      "min_p": 0.01,
+      "repetition_penalty": 1.0
+    },
+    "minimax": {
+      "temperature": 1.0,
+      "top_p": 0.95,
+      "top_k": 40,
+      "min_p": 0.01,
+      "repetition_penalty": 1.0
+    },
+    "gpt-oss": {
+      "temperature": 1.0,
+      "top_p": 1.0,
+      "top_k": 0,
+      "min_p": 0.01,
+      "repetition_penalty": 1.0
+    },
+    "granite-4": {
+      "temperature": 0.0,
+      "top_p": 1.0,
+      "top_k": 0,
+      "min_p": 0.01,
+      "repetition_penalty": 1.0
+    },
+    "kimi-k2": {
+      "temperature": 0.6,
+      "top_p": 0.95,
+      "top_k": -1,
+      "min_p": 0.01,
+      "repetition_penalty": 1.0
+    },
+    "kimi": {
+      "temperature": 0.6,
+      "top_p": 0.95,
+      "top_k": -1,
+      "min_p": 0.01,
+      "repetition_penalty": 1.0
+    },
+    "lfm2": {
+      "temperature": 0.1,
+      "top_p": 0.1,
+      "top_k": 50,
+      "min_p": 0.15,
+      "repetition_penalty": 1.05
+    },
+    "smollm": {
+      "temperature": 0.7,
+      "top_p": 0.95,
+      "top_k": -1,
+      "min_p": 0.01,
+      "repetition_penalty": 1.0
+    },
+    "olmo": {
+      "temperature": 0.7,
+      "top_p": 0.95,
+      "top_k": -1,
+      "min_p": 0.01,
+      "repetition_penalty": 1.0
+    },
+    "falcon": {
+      "temperature": 0.7,
+      "top_p": 0.95,
+      "top_k": -1,
+      "min_p": 0.01,
+      "repetition_penalty": 1.0
+    },
+    "ernie": {
+      "temperature": 0.7,
+      "top_p": 0.95,
+      "top_k": -1,
+      "min_p": 0.01,
+      "repetition_penalty": 1.0
+    },
+    "seed": {
+      "temperature": 0.7,
+      "top_p": 0.95,
+      "top_k": -1,
+      "min_p": 0.01,
+      "repetition_penalty": 1.0
+    },
+    "grok": {
+      "temperature": 1.0,
+      "top_p": 0.95,
+      "top_k": -1,
+      "min_p": 0.01,
+      "repetition_penalty": 1.0
+    },
+    "mimo": {
+      "temperature": 0.7,
+      "top_p": 0.95,
+      "top_k": -1,
+      "min_p": 0.01,
+      "repetition_penalty": 1.0
+    }
+  },
+  "patterns": [
+    "qwen3.6", "qwen3.5",
+    "qwen3-coder", "qwen3-next", "qwen3-vl", "qwen3",
+    "qwen2.5-coder", "qwen2.5-vl", "qwen2.5-omni", "qwen2.5-math", "qwen2.5",
+    "qwen2-vl", "qwen2",
+    "qwq",
+    "gemma-4", "gemma-3n", "gemma-3", "medgemma", "gemma-2",
+    "llama-4", "llama-3.3", "llama-3.2", "llama-3.1", "llama-3",
+    "phi-4", "phi-3",
+    "mistral-nemo", "mistral-small", "mistral-large", "magistral", "ministral",
+    "devstral", "pixtral",
+    "deepseek-r1", "deepseek-v3", "deepseek-ocr",
+    "glm-5", "glm-4",
+    "nemotron",
+    "minimax-m2.5", "minimax",
+    "gpt-oss", "granite-4",
+    "kimi-k2", "kimi",
+    "lfm2", "smollm", "olmo", "falcon", "ernie", "seed", "grok", "mimo"
+  ]
+}

studio/backend/assets/configs/lora_text.yaml

@@ -0,0 +1,42 @@
+model: unsloth/Qwen2.5-0.5B
+
+data:
+  dataset: tatsu-lab/alpaca
+  format_type: auto
+
+training:
+  training_type: lora
+  max_seq_length: 2048
+  load_in_4bit: true
+  output_dir: outputs
+  num_epochs: 1
+  learning_rate: 0.0002
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 0
+  save_steps: 0
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: false
+  gradient_checkpointing: "unsloth"
+
+lora:
+  lora_r: 64
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules: "q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj"
+  vision_all_linear: false
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: unsloth-training
+  enable_tensorboard: false
+  tensorboard_dir: runs

studio/backend/assets/configs/model_defaults/default.yaml

@@ -0,0 +1,56 @@
+# Default model training parameters
+# Used for models without specific configurations
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_ratio: 0.1
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 0.7
+  top_p: 0.95
+  top_k: -1
+  min_p: 0.01
+

studio/backend/assets/configs/model_defaults/embedding/unsloth_Qwen3-Embedding-0.6B.yaml

@@ -0,0 +1,43 @@
+# Model defaults for unsloth/Qwen3-Embedding-0.6B
+# Based on Qwen3_Embedding_(0_6B).py embedding notebook
+# Also applies to: unsloth/Qwen3-Embedding-4B
+
+training:
+  max_seq_length: 512
+  # num_epochs: 2
+  num_epochs: 0
+  learning_rate: 3e-5
+  batch_size: 256
+  gradient_accumulation_steps: 1
+  warmup_ratio: 0.03
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: false
+  gradient_checkpointing: false
+  optim: "adamw_8bit"
+  lr_scheduler_type: "constant_with_warmup"
+
+lora:
+  lora_r: 32
+  lora_alpha: 32
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "embedding-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 50

studio/backend/assets/configs/model_defaults/embedding/unsloth_all-MiniLM-L6-v2.yaml

@@ -0,0 +1,39 @@
+# Model defaults for unsloth/all-MiniLM-L6-v2
+# Based on All_MiniLM_L6_v2.py embedding notebook
+
+training:
+  max_seq_length: 512
+  # num_epochs: 2
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 256
+  gradient_accumulation_steps: 1
+  warmup_ratio: 0.03
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: false
+  gradient_checkpointing: false
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 64
+  lora_alpha: 128
+  lora_dropout: 0.0
+  target_modules:
+    - "value"
+    - "key"
+    - "dense"
+    - "query"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "embedding-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 50

studio/backend/assets/configs/model_defaults/embedding/unsloth_bge-m3.yaml

@@ -0,0 +1,39 @@
+# Model defaults for unsloth/bge-m3
+# Based on BGE_M3.py embedding notebook
+
+training:
+  max_seq_length: 512
+  # num_epochs: 2
+  num_epochs: 0
+  learning_rate: 3e-5
+  batch_size: 256
+  gradient_accumulation_steps: 1
+  warmup_ratio: 0.03
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: false
+  gradient_checkpointing: false
+  optim: "adamw_8bit"
+  lr_scheduler_type: "constant_with_warmup"
+
+lora:
+  lora_r: 32
+  lora_alpha: 64
+  lora_dropout: 0.0
+  target_modules:
+    - "key"
+    - "query"
+    - "dense"
+    - "value"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "embedding-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 50

studio/backend/assets/configs/model_defaults/embedding/unsloth_embeddinggemma-300m.yaml

@@ -0,0 +1,42 @@
+# Model defaults for unsloth/embeddinggemma-300m
+# Based on EmbeddingGemma_(300M).py embedding notebook
+
+training:
+  max_seq_length: 1024
+  # num_epochs: 1
+  num_epochs: 0
+  learning_rate: 2e-5
+  batch_size: 64
+  gradient_accumulation_steps: 2
+  warmup_ratio: 0.03
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: false
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 32
+  lora_alpha: 64
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "embedding-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 5

studio/backend/assets/configs/model_defaults/embedding/unsloth_gte-modernbert-base.yaml

@@ -0,0 +1,38 @@
+# Model defaults for unsloth/gte-modernbert-base
+# Based on ModernBert.py embedding notebook
+
+training:
+  max_seq_length: 512
+  # num_epochs: 2
+  num_epochs: 0
+  learning_rate: 3e-5
+  batch_size: 256
+  gradient_accumulation_steps: 1
+  warmup_ratio: 0.03
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: false
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "constant_with_warmup"
+
+lora:
+  lora_r: 64
+  lora_alpha: 128
+  lora_dropout: 0.0
+  target_modules:
+    - "Wi"
+    - "Wo"
+    - "Wqkv"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "embedding-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 50

studio/backend/assets/configs/model_defaults/ernie/unsloth_ERNIE-4.5-21B-A3B-PT.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/ERNIE-4.5-21B-A3B-PT
+# Based on ERNIE_4_5_21B_A3B_PT-Conversational.ipynb
+# Also applies to: unsloth/ERNIE-4.5-21B-A3B-PT
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 4
+  gradient_accumulation_steps: 2
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 8
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/ernie/unsloth_ERNIE-4.5-VL-28B-A3B-PT.yaml

@@ -0,0 +1,55 @@
+# Model defaults for unsloth/ERNIE-4.5-VL-28B-A3B-PT
+# Based on ERNIE_4_5_VL_28B_A3B_PT_Vision.ipynb
+# Also applies to: unsloth/ERNIE-4.5-VL-28B-A3B-PT
+# added inference parameters from unsloth notebook
+
+training:
+  trust_remote_code: true
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 2
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 8
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: true
+  temperature: 1.5
+  min_p: 0.1
+

studio/backend/assets/configs/model_defaults/falcon/tiiuae_Falcon-H1-0.5B-Instruct.yaml

@@ -0,0 +1,47 @@
+# Model defaults for tiiuae/Falcon-H1-0.5B-Instruct
+# Based on Falcon_H1_(0.5B)-Alpaca.ipynb
+# Also applies to: tiiuae/Falcon-H1-0.5B-Instruct, unsloth/Falcon-H1-0.5B-Instruct
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 8
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: false
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.1
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/gemma/unsloth_codegemma-7b-bnb-4bit.yaml

@@ -0,0 +1,50 @@
+# Model defaults for unsloth/codegemma-7b-bnb-4bit
+# Based on CodeGemma_(7B)-Conversational.ipynb
+# Also applies to: unsloth/codegemma-7b, google/codegemma-7b
+# added inference parameters from Ollama
+
+training:
+  trust_remote_code: false
+  max_seq_length: 4096
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 1
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 0
+  top_p: 0.9

studio/backend/assets/configs/model_defaults/gemma/unsloth_functiongemma-270m-it.yaml

@@ -0,0 +1,53 @@
+# Model defaults for unsloth/functiongemma-270m-it
+# Based on FunctionGemma_(270M).ipynb
+# Also applies to: unsloth/functiongemma-270m-it-unsloth-bnb-4bit, google/functiongemma-270m-it, unsloth/functiongemma-270m-it-unsloth-bnb-4bit
+# added inference parameters from unsloth guides
+
+training:
+  trust_remote_code: false
+  max_seq_length: 4096
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 4
+  gradient_accumulation_steps: 2
+  warmup_steps: 10
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 128
+  lora_alpha: 256
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.0
+  top_k: 64
+  top_p: 0.95
+  min_p: 0.0
+

studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-2-27b-bnb-4bit.yaml

@@ -0,0 +1,46 @@
+# Model defaults for unsloth/gemma-2-27b-bnb-4bit
+# Based on Gemma2_(9B)-Alpaca.ipynb (same defaults for larger models)
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-2-2b.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/gemma-2-2b
+# Based on Gemma2_(2B)-Alpaca.ipynb
+# Also applies to: unsloth/gemma-2-2b-bnb-4bit, google/gemma-2-2b
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-3-270m-it.yaml

@@ -0,0 +1,53 @@
+# Model defaults for unsloth/gemma-3-270m-it
+# Based on Gemma3_(270M).ipynb
+# Also applies to: unsloth/gemma-3-270m-it-unsloth-bnb-4bit, google/gemma-3-270m-it, unsloth/gemma-3-270m-it-bnb-4bit
+# added inference parameters from unsloth guides
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 5e-5
+  batch_size: 4
+  gradient_accumulation_steps: 1
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 128
+  lora_alpha: 128
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.0
+  top_k: 64
+  top_p: 0.95
+  min_p: 0.0
+

studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-3-27b-it.yaml

@@ -0,0 +1,51 @@
+# Model defaults for unsloth/gemma-3-27b-it
+# Based on Gemma3_(27B)_A100-Conversational.ipynb
+# Also applies to: unsloth/gemma-3-27b-it-unsloth-bnb-4bit, google/gemma-3-27b-it, unsloth/gemma-3-27b-it-bnb-4bit
+# added inference parameters from unsloth guides
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 8
+  lora_alpha: 8
+  lora_dropout: 0.0
+  target_modules:
+    - "all-linear"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.0
+  top_k: 64
+  top_p: 0.95
+  min_p: 0.0
+

studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-3-4b-it.yaml

@@ -0,0 +1,51 @@
+# Model defaults for unsloth/gemma-3-4b-it
+# Based on Gemma3_(4B).ipynb
+# Also applies to: unsloth/gemma-3-4b-it-unsloth-bnb-4bit, google/gemma-3-4b-it, unsloth/gemma-3-4b-it-bnb-4bit
+# added inference parameters from unsloth guides
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 8
+  lora_alpha: 8
+  lora_dropout: 0.0
+  target_modules:
+    - "all-linear"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.0
+  top_k: 64
+  top_p: 0.95
+  min_p: 0.0
+

studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-3-4b-pt.yaml

@@ -0,0 +1,51 @@
+# Model defaults for unsloth/gemma-3-4b-pt
+# Based on Gemma3_(4B)-Vision.ipynb
+# Also applies to: unsloth/gemma-3-4b-pt-unsloth-bnb-4bit, google/gemma-3-4b-pt, unsloth/gemma-3-4b-pt-bnb-4bit
+# added inference parameters from unsloth guides
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 2
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 1
+  gradient_accumulation_steps: 4
+  warmup_ratio: 0.03
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: true
+  optim: "adamw_torch_fused"
+  lr_scheduler_type: "cosine"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "all-linear"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.0
+  top_k: 64
+  top_p: 0.95
+  min_p: 0.0
+

studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-3n-E4B-it.yaml

@@ -0,0 +1,53 @@
+# Model defaults for unsloth/gemma-3n-E4B-it
+# Based on Gemma3N_(4B)-Conversational.ipynb
+# Also applies to: unsloth/gemma-3n-E4B-it-unsloth-bnb-4bit, google/gemma-3n-E4B-it, unsloth/gemma-3n-E4B-it-unsloth-bnb-4bit
+# added inference parameters from unsloth guides
+
+training:
+  trust_remote_code: false
+  max_seq_length: 1024
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 1
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 8
+  lora_alpha: 8
+  lora_dropout: 0.0
+  target_modules:
+    - "all-linear"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+audio_input: true
+
+inference:
+  trust_remote_code: false
+  temperature: 1.0
+  top_k: 64
+  top_p: 0.95
+  min_p: 0.0
+

studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-3n-E4B.yaml

@@ -0,0 +1,53 @@
+# Model defaults for unsloth/gemma-3n-E4B
+# Based on Gemma3N_(4B)-Vision.ipynb
+# Also applies to: unsloth/gemma-3n-E4B-unsloth-bnb-4bit, google/gemma-3n-E4B
+# added inference parameters from unsloth guides
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 2
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 1
+  gradient_accumulation_steps: 4
+  warmup_ratio: 0.03
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: true
+  optim: "adamw_torch_fused"
+  lr_scheduler_type: "cosine"
+
+lora:
+  lora_r: 32
+  lora_alpha: 32
+  lora_dropout: 0.0
+  target_modules:
+    - "all-linear"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+audio_input: true
+
+inference:
+  trust_remote_code: false
+  temperature: 1.0
+  top_k: 64
+  top_p: 0.95
+  min_p: 0.0
+

studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-4-26B-A4B-it.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/gemma-4-26B-A4B-it
+# Also applies to: google/gemma-4-26B-A4B-it, unsloth/gemma-4-26B-A4B-it-GGUF
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 8
+  lora_alpha: 8
+  lora_dropout: 0.0
+  target_modules:
+    - "all-linear"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.0
+  top_p: 0.95
+  top_k: 64
+  min_p: 0.0

studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-4-26B-A4B.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/gemma-4-26B-A4B (base/pretrained)
+# Also applies to: google/gemma-4-26B-A4B
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 8
+  lora_alpha: 8
+  lora_dropout: 0.0
+  target_modules:
+    - "all-linear"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.0
+  top_p: 0.95
+  top_k: 64
+  min_p: 0.0

studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-4-31B-it.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/gemma-4-31B-it
+# Also applies to: google/gemma-4-31B-it, unsloth/gemma-4-31B-it-GGUF
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 8
+  lora_alpha: 8
+  lora_dropout: 0.0
+  target_modules:
+    - "all-linear"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.0
+  top_p: 0.95
+  top_k: 64
+  min_p: 0.0

studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-4-31B.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/gemma-4-31B (base/pretrained)
+# Also applies to: google/gemma-4-31B
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 8
+  lora_alpha: 8
+  lora_dropout: 0.0
+  target_modules:
+    - "all-linear"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.0
+  top_p: 0.95
+  top_k: 64
+  min_p: 0.0

studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-4-E2B-it.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/gemma-4-E2B-it
+# Also applies to: google/gemma-4-E2B-it, unsloth/gemma-4-E2B-it-GGUF
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 8
+  lora_alpha: 8
+  lora_dropout: 0.0
+  target_modules:
+    - "all-linear"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.0
+  top_p: 0.95
+  top_k: 64
+  min_p: 0.0

studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-4-E2B.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/gemma-4-E2B (base/pretrained)
+# Also applies to: google/gemma-4-E2B
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 8
+  lora_alpha: 8
+  lora_dropout: 0.0
+  target_modules:
+    - "all-linear"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.0
+  top_p: 0.95
+  top_k: 64
+  min_p: 0.0

studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-4-E4B-it.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/gemma-4-E4B-it
+# Also applies to: google/gemma-4-E4B-it, unsloth/gemma-4-E4B-it-GGUF
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 8
+  lora_alpha: 8
+  lora_dropout: 0.0
+  target_modules:
+    - "all-linear"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.0
+  top_p: 0.95
+  top_k: 64
+  min_p: 0.0

studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-4-E4B.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/gemma-4-E4B (base/pretrained)
+# Also applies to: google/gemma-4-E4B
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 8
+  lora_alpha: 8
+  lora_dropout: 0.0
+  target_modules:
+    - "all-linear"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.0
+  top_p: 0.95
+  top_k: 64
+  min_p: 0.0

studio/backend/assets/configs/model_defaults/gpt-oss/unsloth_gpt-oss-120b.yaml

@@ -0,0 +1,52 @@
+# Model defaults for unsloth/gpt-oss-120b
+# Based on gpt-oss-(120B)_A100-Fine-tuning.ipynb
+# Also applies to: openai/gpt-oss-120b, unsloth/gpt-oss-120b-unsloth-bnb-4bit
+# added inference parameters from unsloth guides
+
+training:
+  trust_remote_code: false
+  max_seq_length: 4096
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 4
+  gradient_accumulation_steps: 1
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 32
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.0
+  top_p: 1.0
+  top_k: 0
+

studio/backend/assets/configs/model_defaults/gpt-oss/unsloth_gpt-oss-20b.yaml

@@ -0,0 +1,52 @@
+# Model defaults for unsloth/gpt-oss-20b
+# Based on gpt-oss-(20B)-Fine-tuning.ipynb
+# Also applies to: openai/gpt-oss-20b, unsloth/gpt-oss-20b-unsloth-bnb-4bit, unsloth/gpt-oss-20b-BF16
+# added inference parameters from unsloth guides
+
+training:
+  trust_remote_code: false
+  max_seq_length: 1024
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 1
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 8
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.0
+  top_p: 1.0
+  top_k: 0
+

studio/backend/assets/configs/model_defaults/granite/unsloth_granite-4.0-350m-unsloth-bnb-4bit.yaml

@@ -0,0 +1,54 @@
+# Model defaults for unsloth/granite-4.0-350m
+# Based on Granite4.0_350M.ipynb
+# Also applies to: ibm-granite/granite-4.0-350m, unsloth/granite-4.0-350m-bnb-4bit
+# added inference parameters from unsloth guides
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 32
+  lora_alpha: 32
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+    - "shared_mlp.input_linear"
+    - "shared_mlp.output_linear"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 0.0
+  top_p: 1.0
+  top_k: 0
+

studio/backend/assets/configs/model_defaults/granite/unsloth_granite-4.0-h-micro.yaml

@@ -0,0 +1,54 @@
+# Model defaults for unsloth/granite-4.0-h-micro
+# Based on Granite4.0.ipynb
+# Also applies to: ibm-granite/granite-4.0-h-micro, unsloth/granite-4.0-h-micro-bnb-4bit, unsloth/granite-4.0-h-micro-unsloth-bnb-4bit
+# added inference parameters from unsloth guides
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 32
+  lora_alpha: 32
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+    - "shared_mlp.input_linear"
+    - "shared_mlp.output_linear"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 0.0
+  top_p: 1.0
+  top_k: 0
+

studio/backend/assets/configs/model_defaults/llama/unsloth_Llama-3.2-11B-Vision-Instruct.yaml

@@ -0,0 +1,49 @@
+# Model defaults for unsloth/Llama-3.2-11B-Vision-Instruct
+# Based on Llama3.2_(11B)-Vision.ipynb
+# Also applies to: unsloth/Llama-3.2-11B-Vision-Instruct-unsloth-bnb-4bit, meta-llama/Llama-3.2-11B-Vision-Instruct, unsloth/Llama-3.2-11B-Vision-Instruct-bnb-4bit
+# added inference parameters from unsloth notebook
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "all-linear"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.5
+  min_p: 0.1
+

studio/backend/assets/configs/model_defaults/llama/unsloth_Llama-3.2-1B-Instruct.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/Llama-3.2-1B-Instruct
+# Based on Llama3.2_(1B)-RAFT.ipynb
+# Also applies to: unsloth/Llama-3.2-1B-Instruct-unsloth-bnb-4bit, meta-llama/Llama-3.2-1B-Instruct, unsloth/Llama-3.2-1B-Instruct-bnb-4bit, RedHatAI/Llama-3.2-1B-Instruct-FP8, unsloth/Llama-3.2-1B-Instruct-FP8-Block, unsloth/Llama-3.2-1B-Instruct-FP8-Dynamic
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 5
+  num_epochs: 0
+  learning_rate: 2e-5
+  batch_size: 1
+  gradient_accumulation_steps: 8
+  warmup_steps: 0
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: true
+  optim: "adamw_torch"
+  lr_scheduler_type: "cosine"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/llama/unsloth_Llama-3.2-3B-Instruct.yaml

@@ -0,0 +1,51 @@
+# Model defaults for unsloth/Llama-3.2-3B-Instruct
+# Based on Llama3.2_(1B_and_3B)-Conversational.ipynb
+# Also applies to: unsloth/Llama-3.2-3B-Instruct-unsloth-bnb-4bit, meta-llama/Llama-3.2-3B-Instruct, unsloth/Llama-3.2-3B-Instruct-bnb-4bit, RedHatAI/Llama-3.2-3B-Instruct-FP8, unsloth/Llama-3.2-3B-Instruct-FP8-Block, unsloth/Llama-3.2-3B-Instruct-FP8-Dynamic
+# added inference parameters from unsloth notebook
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.5
+  min_p: 0.1
+

studio/backend/assets/configs/model_defaults/llama/unsloth_Llama-3.3-70B-Instruct.yaml

@@ -0,0 +1,51 @@
+# Model defaults for unsloth/Llama-3.3-70B-Instruct
+# Based on Llama3.3_(70B)_A100-Conversational.ipynb
+# Also applies to: unsloth/Llama-3.3-70B-Instruct-unsloth-bnb-4bit, meta-llama/Llama-3.3-70B-Instruct, unsloth/Llama-3.3-70B-Instruct-bnb-4bit, RedHatAI/Llama-3.3-70B-Instruct-FP8, unsloth/Llama-3.3-70B-Instruct-FP8-Block, unsloth/Llama-3.3-70B-Instruct-FP8-Dynamic
+# added inference parameters from unsloth notebook
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.5
+  min_p: 0.1
+

studio/backend/assets/configs/model_defaults/llama/unsloth_Meta-Llama-3.1-70B-bnb-4bit.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/Meta-Llama-3.1-70B-bnb-4bit
+# Based on Llama3.1_(8B)-Alpaca.ipynb
+# Also applies to: unsloth/Meta-Llama-3.1-8B-bnb-4bit, unsloth/Meta-Llama-3.1-8B-unsloth-bnb-4bit, meta-llama/Meta-Llama-3.1-8B, unsloth/Meta-Llama-3.1-8B, unsloth/Meta-Llama-3.1-70B, meta-llama/Meta-Llama-3.1-70B, unsloth/Meta-Llama-3.1-405B-bnb-4bit, meta-llama/Meta-Llama-3.1-405B
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/llama/unsloth_Meta-Llama-3.1-8B-Instruct-bnb-4bit.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
+# Based on Llama3.1_(8B)-Inference.ipynb
+# Also applies to: "unsloth/Meta-Llama-3.1-8B-Instruct-unsloth-bnb-4bit", "meta-llama/Meta-Llama-3.1-8B-Instruct", "unsloth/Meta-Llama-3.1-8B-Instruct","RedHatAI/Llama-3.1-8B-Instruct-FP8","unsloth/Llama-3.1-8B-Instruct-FP8-Block","unsloth/Llama-3.1-8B-Instruct-FP8-Dynamic"
+
+training:
+  trust_remote_code: false
+  max_seq_length: 8192
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/llama/unsloth_llama-3-8b-Instruct-bnb-4bit.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/llama-3-8b-Instruct-bnb-4bit
+# Based on Llama3_(8B)-Conversational.ipynb
+# Also applies to: unsloth/llama-3-8b-Instruct, meta-llama/Meta-Llama-3-8B-Instruct
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/llama/unsloth_llama-3-8b-bnb-4bit.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/llama-3-8b-bnb-4bit
+# Based on Llama3_(8B)-Alpaca.ipynb
+# Also applies to: unsloth/llama-3-8b, meta-llama/Meta-Llama-3-8B
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/llasa/unsloth_Llasa-3B.yaml

@@ -0,0 +1,46 @@
+# Model defaults for unsloth/Llasa-3B
+# Based on Llasa_TTS_(3B).ipynb and Llasa_TTS_(1B).ipynb
+# Also applies to: HKUSTAudio/Llasa-1B
+# added inference parameters from unsloth notebook
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 5e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 128
+  lora_alpha: 128
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "v_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 1.2
+  top_p: 1.2
+

studio/backend/assets/configs/model_defaults/mistral/unsloth_Magistral-Small-2509-unsloth-bnb-4bit.yaml

@@ -0,0 +1,56 @@
+# Model defaults for unsloth/Magistral-Small-2509
+# Based on Magistral_(24B)-Reasoning-Conversational.ipynb
+# Also applies to: mistralai/Magistral-Small-2509, unsloth/Magistral-Small-2509-bnb-4bit
+# added inference parameters from unsloth guides
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 2
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 32
+  lora_alpha: 32
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 0.7
+  min_p: 0.01
+  top_p: 0.95
+

studio/backend/assets/configs/model_defaults/mistral/unsloth_Ministral-3-3B-Instruct-2512.yaml

@@ -0,0 +1,55 @@
+# Model defaults for unsloth/Ministral-3-3B-Instruct-2512
+# Based on Ministral_3_VL_(3B)_Vision.ipynb
+# Also applies to: unsloth/Ministral-3-3B-Instruct-2512
+# added inference parameters from unsloth guides
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 4
+  gradient_accumulation_steps: 2
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 32
+  lora_alpha: 32
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+  finetune_vision_layers: true
+  finetune_language_layers: true
+  finetune_attention_modules: true
+  finetune_mlp_modules: true
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false
+  temperature: 0.15
+  top_p: 0.95
+

studio/backend/assets/configs/model_defaults/mistral/unsloth_Mistral-Nemo-Base-2407-bnb-4bit.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/Mistral-Nemo-Base-2407-bnb-4bit
+# Based on Mistral_Nemo_(12B)-Alpaca.ipynb
+# Also applies to:  "unsloth/Mistral-Nemo-Base-2407",  "mistralai/Mistral-Nemo-Base-2407", "unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit", "unsloth/Mistral-Nemo-Instruct-2407", "mistralai/Mistral-Nemo-Instruct-2407",
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 2
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false

studio/backend/assets/configs/model_defaults/mistral/unsloth_Mistral-Small-Instruct-2409.yaml

@@ -0,0 +1,47 @@
+# Model defaults for unsloth/Mistral-Small-Instruct-2409
+# Based on Mistral_Small_(22B)-Alpaca.ipynb 
+# Also applies to: unsloth/Mistral-Small-Instruct-2409-bnb-4bit, mistralai/Mistral-Small-Instruct-2409
+
+training:
+  trust_remote_code: false
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 1
+  gradient_accumulation_steps: 4
+  warmup_steps: 5
+  max_steps: 30
+  save_steps: 30
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  trust_remote_code: false