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UI Changes (#4782)

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* UI Changes

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* Remove unrelated test file

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11 changed files with 267 additions and 6 deletions
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20
studio/backend/core/inference/defaults.py
View file

@ -6,6 +6,14 @@
import utils.hardware.hardware as hw
DEFAULT_MODELS_GGUF = [
"unsloth/gemma-4-E2B-it-GGUF",
"unsloth/gemma-4-E4B-it-GGUF",
"unsloth/gemma-4-31B-it-GGUF",
"unsloth/gemma-4-26B-A4B-it-GGUF",
"unsloth/Qwen3.5-4B-GGUF",
"unsloth/Qwen3.5-9B-GGUF",
"unsloth/Qwen3.5-35B-A3B-GGUF",
"unsloth/Qwen3.5-0.8B-GGUF",
"unsloth/Llama-3.2-1B-Instruct-GGUF",
"unsloth/Llama-3.2-3B-Instruct-GGUF",
"unsloth/Llama-3.1-8B-Instruct-GGUF",
@ -15,6 +23,18 @@ DEFAULT_MODELS_GGUF = [
]
DEFAULT_MODELS_STANDARD = [
"unsloth/gemma-4-E2B-it-GGUF",
"unsloth/gemma-4-E4B-it-GGUF",
"unsloth/gemma-4-31B-it-GGUF",
"unsloth/gemma-4-26B-A4B-it-GGUF",
"unsloth/Qwen3.5-4B-GGUF",
"unsloth/Qwen3.5-9B-GGUF",
"unsloth/Qwen3.5-35B-A3B-GGUF",
"unsloth/Qwen3.5-0.8B-GGUF",
"unsloth/gemma-4-E2B-it",
"unsloth/gemma-4-E4B-it",
"unsloth/gemma-4-31B-it",
"unsloth/gemma-4-26B-A4B-it",
"unsloth/Qwen3-4B-Instruct-2507",
"unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit",
"unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit",

2
studio/backend/core/inference/llama_cpp.py
View file

@ -810,7 +810,9 @@ class LlamaCppBackend:
# Detect tool calling support from chat template
tool_markers = [
"{%- if tools %}",
"{%- if tools -%}",
"{% if tools %}",
"{% if tools -%}",
'"role" == "tool"',
"'role' == 'tool'",
'message.role == "tool"',

2
studio/backend/requirements/extras-no-deps.txt
View file

@ -11,6 +11,6 @@ git+https://github.com/meta-pytorch/OpenEnv.git
# executorch>=1.0.1 # 41.5 MB - no imports in unsloth/zoo/studio
torch-c-dlpack-ext
sentence_transformers==5.2.0
transformers==4.57.6
transformers>=4.57.6
pytorch_tokenizers
kernels

4
studio/backend/requirements/single-env/constraints.txt
View file

@ -1,8 +1,8 @@
# Single-env pins for unsloth + studio + data-designer
# Keep compatible with unsloth transformers bounds.
transformers==4.57.6
transformers>=4.57.6
trl==0.23.1
huggingface-hub==0.36.2
huggingface-hub>=0.36.2
# Studio stack
datasets==4.3.0

6
studio/backend/utils/transformers_version.py
View file

@ -47,6 +47,8 @@ TRANSFORMERS_5_MODEL_SUBSTRINGS: tuple[str, ...] = (
"qwen3.5", # Qwen3.5 family (35B-A3B, etc.)
"qwen3-next", # Qwen3-Next and variants
"tiny_qwen3_moe", # imdatta0/tiny_qwen3_moe_2.8B_0.7B
"gemma-4", # Gemma-4 (E2B-it, E4B-it, 31B-it, 26B-A4B-it)
"gemma4", # Gemma-4 alternate naming
)
# Tokenizer classes that only exist in transformers>=5.x
@ -58,7 +60,7 @@ _TRANSFORMERS_5_TOKENIZER_CLASSES: set[str] = {
_tokenizer_class_cache: dict[str, bool] = {}
# Versions
TRANSFORMERS_5_VERSION = "5.3.0"
TRANSFORMERS_5_VERSION = "5.5.0.dev0"
TRANSFORMERS_DEFAULT_VERSION = "4.57.6"
# Pre-installed directory for transformers 5.x — created by setup.sh / setup.ps1
@ -258,7 +260,7 @@ def _purge_modules() -> int:
_VENV_T5_PACKAGES = (
f"transformers=={TRANSFORMERS_5_VERSION}",
"huggingface_hub==1.7.1",
"huggingface_hub==1.8.0",
"hf_xet==1.4.2",
"tiktoken",
)

4
studio/frontend/src/config/training.ts
View file

@ -141,6 +141,10 @@ export const MODEL_TYPE_TO_HF_TASK: Record<ModelType, PipelineType> = {
export const PRIORITY_TRAINING_MODELS: readonly string[] = [
"unsloth/gemma-4-E2B-it",
"unsloth/gemma-4-E4B-it",
"unsloth/gemma-4-31B-it",
"unsloth/gemma-4-26B-A4B-it",
"unsloth/Qwen3.5-2B",
"unsloth/Qwen3.5-9B",
"unsloth/gpt-oss-20b",

108
unsloth/chat_templates.py
View file

@ -863,6 +863,114 @@ DEFAULT_SYSTEM_MESSAGE["gemma-3n"] = None # No system message in Gemma-3n
CHAT_TEMPLATES["gemma3n"] = (gemma3n_template, gemma3n_template_eos_token, False, gemma3n_ollama,)
DEFAULT_SYSTEM_MESSAGE["gemma3n"] = None # No system message in Gemma-3n
# =========================================== Gemma-4
# Gemma-4 uses <|turn>role\n...<turn|>\n format
gemma4_template = \
"""{%- if messages[0]['role'] == 'system' -%}
{%- set first_user_prefix = messages[0]['content'] + '\n\n' -%}
{%- set loop_messages = messages[1:] -%}
{%- else -%}
{%- set first_user_prefix = "" -%}
{%- set loop_messages = messages -%}
{%- endif -%}
{%- for message in loop_messages -%}
{%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
{{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
{%- endif -%}
{%- if (message['role'] == 'assistant') -%}
{%- set role = "model" -%}
{%- else -%}
{%- set role = message['role'] -%}
{%- endif -%}
{{ '<|turn>' + role + '\n' + (first_user_prefix if loop.first else "") }}
{%- if message['content'] is string -%}
{{ message['content'] | trim }}
{%- elif message['content'] is iterable -%}
{%- for item in message['content'] -%}
{%- if item['type'] == 'audio' -%}
{{ '<|audio|>' }}
{%- elif item['type'] == 'image' -%}
{{ '<|image|>' }}
{%- elif item['type'] == 'video' -%}
{{ '<|video|>' }}
{%- elif item['type'] == 'text' -%}
{{ item['text'] | trim }}
{%- endif -%}
{%- endfor -%}
{%- else -%}
{{ raise_exception("Invalid content type") }}
{%- endif -%}
{{ '<turn|>\n' }}
{%- endfor -%}
{%- if add_generation_prompt -%}
{{'<|turn>model\n'}}
{%- endif -%}
"""
try:
gemma4_ollama = _ollama_template("gemma-4")
except KeyError:
gemma4_ollama = ""
gemma4_template_eos_token = "<turn|>"
CHAT_TEMPLATES["gemma-4"] = (gemma4_template, gemma4_template_eos_token, False, gemma4_ollama,)
DEFAULT_SYSTEM_MESSAGE["gemma-4"] = None
CHAT_TEMPLATES["gemma4"] = (gemma4_template, gemma4_template_eos_token, False, gemma4_ollama,)
DEFAULT_SYSTEM_MESSAGE["gemma4"] = None
# Gemma-4 with empty thought channel (required for larger models like 31B, 26B-A4B)
# Injects <|channel>thought\n<channel|> at the start of each model response during training
gemma4_thinking_template = \
"""{%- if messages[0]['role'] == 'system' -%}
{%- set first_user_prefix = messages[0]['content'] + '\n\n' -%}
{%- set loop_messages = messages[1:] -%}
{%- else -%}
{%- set first_user_prefix = "" -%}
{%- set loop_messages = messages -%}
{%- endif -%}
{%- for message in loop_messages -%}
{%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
{{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
{%- endif -%}
{%- if (message['role'] == 'assistant') -%}
{%- set role = "model" -%}
{%- else -%}
{%- set role = message['role'] -%}
{%- endif -%}
{{ '<|turn>' + role + '\n' + (first_user_prefix if loop.first else "") }}
{%- if role == "model" -%}
{{ '<|channel>thought\n<channel|>' }}
{%- endif -%}
{%- if message['content'] is string -%}
{{ message['content'] | trim }}
{%- elif message['content'] is iterable -%}
{%- for item in message['content'] -%}
{%- if item['type'] == 'audio' -%}
{{ '<|audio|>' }}
{%- elif item['type'] == 'image' -%}
{{ '<|image|>' }}
{%- elif item['type'] == 'video' -%}
{{ '<|video|>' }}
{%- elif item['type'] == 'text' -%}
{{ item['text'] | trim }}
{%- endif -%}
{%- endfor -%}
{%- else -%}
{{ raise_exception("Invalid content type") }}
{%- endif -%}
{{ '<turn|>\n' }}
{%- endfor -%}
{%- if add_generation_prompt -%}
{{'<|turn>model\n'}}
{%- endif -%}
"""
CHAT_TEMPLATES["gemma-4-thinking"] = (gemma4_thinking_template, gemma4_template_eos_token, False, gemma4_ollama,)
DEFAULT_SYSTEM_MESSAGE["gemma-4-thinking"] = None
CHAT_TEMPLATES["gemma4-thinking"] = (gemma4_thinking_template, gemma4_template_eos_token, False, gemma4_ollama,)
DEFAULT_SYSTEM_MESSAGE["gemma4-thinking"] = None
# =========================================== GPT-OSS
# Obtained via
# print(tokenizer.chat_template.replace("}\n", "####").replace("\n", "\\n").replace("####", "}\n"))

21
unsloth/models/_utils.py
View file

@ -537,6 +537,15 @@ try:
except:
pass
# Gemma4 It is strongly recommended to train Gemma4 models with the `eager`
try:
from transformers.models.gemma4.modeling_gemma4 import logger as gemma4_logger
gemma4_logger.addFilter(HideLoggingMessage("strongly recommended"))
del gemma4_logger
except:
pass
# Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed.
try:
from huggingface_hub.file_download import logger as hub_logger
@ -1930,6 +1939,18 @@ def _unsloth_pre_compute_loss(self, model, inputs, *args, **kwargs):
_has_ccm = _mod is not None and hasattr(_mod, "create_causal_mask_mapping")
if _has_ccm and _inner.training:
inputs["token_type_ids"] = torch.zeros_like(inputs["input_ids"])
# Gemma4 uses mm_token_type_ids (not token_type_ids) for VLM masking
if "mm_token_type_ids" not in inputs and "input_ids" in inputs:
_inner = model
for _attr in ("base_model", "model", "model"):
_inner = getattr(_inner, _attr, _inner)
if getattr(getattr(_inner, "config", None), "model_type", "") in ("gemma4",):
import sys as _sys
_mod = _sys.modules.get(type(_inner).__module__)
_has_ccm = _mod is not None and hasattr(_mod, "create_causal_mask_mapping")
if _has_ccm and _inner.training:
inputs["mm_token_type_ids"] = torch.zeros_like(inputs["input_ids"])
outputs = self._old_compute_loss(model, inputs, *args, **kwargs)
return outputs

14
unsloth/models/loader.py
View file

@ -78,6 +78,7 @@ SUPPORTS_QWEN3_MOE = transformers_version >= Version("4.50.3")
SUPPORTS_FALCON_H1 = transformers_version >= Version("4.53.0")
SUPPORTS_GEMMA3N = transformers_version >= Version("4.53.0")
SUPPORTS_GPTOSS = transformers_version >= Version("4.55.0")
SUPPORTS_GEMMA4 = transformers_version >= Version("5.5.0.dev0")
# Transformers v5 meta-device loading corrupts non-persistent buffers (inv_freq).
# See _fix_rope_inv_freq() below for details.
_NEEDS_ROPE_FIX = transformers_version >= Version("5.0.0")
@ -107,6 +108,8 @@ FORCE_FLOAT32 = [
"gemma3n",
"gpt_oss",
"qwen3_5", # Qwen3.5 GDN layers produce NaN grad norms in float16 training
"gemma4,", # Add comma bc gemma4 will match gemma4_text
"gemma4_text",
]
global DISABLE_COMPILE_MODEL_NAMES
@ -1130,6 +1133,17 @@ class FastModel(FastBaseModel):
raise RuntimeError(
"Unsloth: Qwen 2.5 only works on transformers >= 4.49.0." + LATEST
)
# Gemma 4 must be before Gemma 3N and Gemma 3
elif "gemma4" in model_types_all:
if not SUPPORTS_GEMMA4:
raise RuntimeError(
"Unsloth: Gemma 4 requires transformers >= 5.5.0" + LATEST
)
os.environ["UNSLOTH_DISABLE_STATIC_GENERATION"] = "1"
os.environ["UNSLOTH_HIGH_PRECISION_LAYERNORM"] = "1"
# Disable flex_attention for Gemma-4: flex compile overhead is 2.7x slower
# than SDPA. Our attention patch ensures Q/K/V dtype alignment for SDPA.
os.environ["UNSLOTH_ENABLE_FLEX_ATTENTION"] = "0"
# Gemma 3N must be before Gemma 3
elif "gemma3n" in model_types_all:
if transformers_version < Version("4.53.0"):

67
unsloth/models/vision.py
View file

@ -216,6 +216,10 @@ def unsloth_base_fast_generate(
kwargs["pixel_values"] = kwargs["pixel_values"].to(dtype)
except:
pass
try:
kwargs["pixel_values_videos"] = kwargs["pixel_values_videos"].to(dtype)
except:
pass
# Mixed precision autocast
if os.environ.get("UNSLOTH_FORCE_FLOAT32", "0") == "1":
@ -1029,6 +1033,15 @@ class FastBaseModel:
f"Unsloth: Warning - VLM processor fallback returned None for model_type={model_type_arch}",
file = sys.stderr,
)
# Backwards compat: if processor has no chat_template (e.g. old saves without
# chat_template.jinja) but the inner tokenizer does, copy it to the processor.
if (
hasattr(tokenizer, "tokenizer")
and getattr(tokenizer, "chat_template", None) is None
and getattr(tokenizer.tokenizer, "chat_template", None) is not None
):
tokenizer.chat_template = tokenizer.tokenizer.chat_template
if hasattr(tokenizer, "tokenizer"):
__tokenizer = tokenizer.tokenizer
# Add padding side as well
@ -1285,7 +1298,59 @@ class FastBaseModel:
model,
use_gradient_checkpointing = use_gradient_checkpointing,
)
# Gemma4 ClippableLinear wraps nn.Linear -- PEFT can't inject LoRA on it directly.
# Monkey-patch PEFT to target the inner .linear child instead.
_clippable_linear_cls = None
try:
from transformers.models.gemma4.modeling_gemma4 import (
Gemma4ClippableLinear as _clippable_linear_cls,
)
except ImportError:
pass
if _clippable_linear_cls is not None:
from peft.tuners.lora.model import LoraModel as _LoraModel
_original_car = _LoraModel._create_and_replace
def _patched_car(
self,
peft_config,
adapter_name,
target,
target_name,
parent,
current_key = None,
**kwargs,
):
if isinstance(target, _clippable_linear_cls):
return _original_car(
self,
peft_config,
adapter_name,
target.linear,
"linear",
target,
current_key = current_key,
**kwargs,
)
return _original_car(
self,
peft_config,
adapter_name,
target,
target_name,
parent,
current_key = current_key,
**kwargs,
)
_LoraModel._create_and_replace = _patched_car
model = _get_peft_model(model, lora_config)
# Restore original PEFT method
if _clippable_linear_cls is not None:
_LoraModel._create_and_replace = _original_car
# Apply QAT + LoRA if specified
if qat_scheme is not None:
print("Unsloth: Applying QAT to mitigate quantization degradation")
@ -1383,7 +1448,7 @@ class FastBaseModel:
# after this point, so we intercept gradient_checkpointing_enable
# to always force use_reentrant=True for Gemma3N.
_model_type = getattr(getattr(model, "config", None), "model_type", "") or ""
if "gemma3n" in _model_type.lower():
if "gemma3n" in _model_type.lower() or "gemma4" in _model_type.lower():
_original_gc_enable = model.gradient_checkpointing_enable
def _gc_enable_reentrant(**kwargs):

25
unsloth/ollama_template_mappers.py
View file

@ -1199,6 +1199,21 @@ TEMPLATE """{{- range $i, $_ := .Messages }}
OLLAMA_TEMPLATES["gemma-3n"] = gemma3n_ollama
OLLAMA_TEMPLATES["gemma3n"] = gemma3n_ollama
# =========================================== Gemma-4
gemma4_ollama = '''
FROM {__FILE_LOCATION__}
TEMPLATE """{{- range $i, $_ := .Messages }}
{{- $last := eq (len (slice $.Messages $i)) 1 }}
<|turn>{{ .Role }}
{{ .Content }}{{ if not $last }}<turn|>
{{ end }}
{{- end }}<turn|>
<|turn>model
"""
'''
OLLAMA_TEMPLATES["gemma-4"] = gemma4_ollama
OLLAMA_TEMPLATES["gemma4"] = gemma4_ollama
# =========================================== GPT-OSS
# Ollama from https://ollama.com/library/gpt-oss:latest/blobs/fa6710a93d78
@ -1961,6 +1976,16 @@ OLLAMA_TEMPLATE_TO_MODEL_MAPPER = {
"google/medgemma-27b-text-it",
"unsloth/medgemma-27b-text-it-bnb-4bit",
),
"gemma4": (
"unsloth/gemma-4-E2B-it",
"unsloth/gemma-4-E2B",
"unsloth/gemma-4-E4B-it",
"unsloth/gemma-4-E4B",
"unsloth/gemma-4-31B-it",
"unsloth/gemma-4-31B",
"unsloth/gemma-4-26B-A4B-it",
"unsloth/gemma-4-26B-A4B",
),
"gemma3n": (
"unsloth/gemma-3n-E4B-it-unsloth-bnb-4bit",
"unsloth/gemma-3n-E4B-it",