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Improve documentation on how to export model from Colab
Related to #1615 Add documentation and function for exporting models from Colab to local machines. * **README.md**: Add a new section titled "Exporting Models from Colab to Local Machine" under "✨ Finetune for Free" with detailed steps for exporting models from Colab to local machines. * **CONTRIBUTING.md**: Add a note about the new documentation section for exporting models from Colab. * **unsloth/save.py**: Add a new function `export_model_to_local` to handle exporting models from Colab to local machines.
This commit is contained in:
Vishwanath Martur
parent
646ad2f141
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0361bd658f
3 changed files with 64 additions and 64 deletions
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@ -24,6 +24,9 @@ Your support extends beyond code:
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- Share how Unsloth powers your projects.
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- Star our repository to show your appreciation.
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## Note
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We have added a new section in the `README.md` under "✨ Finetune for Free" titled "Exporting Models from Colab to Local Machine" with detailed steps. Please refer to it for guidance on exporting models from Colab to your local machine.
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Finally, please be mindful of our [Code of Conduct](https://github.com/unslothai/unsloth/tree/main/unsloth/CODE_OF_CONDUCT.md) to ensure a welcoming and inclusive environment for everyone.
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Thank you so much for reading and we hope you have lots of fun using Unsloth! 🦥
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33
README.md
33
README.md
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@ -39,6 +39,39 @@ All notebooks are **beginner friendly**! Add your dataset, click "Run All", and
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- This [continued pretraining notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_(7B)-CPT.ipynb) is for learning another language
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- Click [here](https://docs.unsloth.ai/) for detailed documentation for Unsloth.
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## Exporting Models from Colab to Local Machine
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If you have fine-tuned a model in Colab and want to use it locally on your machine, follow these steps:
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1. **Save the Model in Colab**: Ensure you have saved the model in a format that can be easily downloaded. You can use the `unsloth_save_model` function to save the model in the desired format.
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2. **Connect to Google Drive**: Mount your Google Drive in Colab to save the model files there. This allows you to download the files to your local machine later.
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```python
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from google.colab import drive
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drive.mount('/content/drive')
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```
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3. **Save Model to Google Drive**: Save the model files to a directory in your Google Drive.
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```python
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model.save_pretrained('/content/drive/MyDrive/your_model_directory')
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tokenizer.save_pretrained('/content/drive/MyDrive/your_model_directory')
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```
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4. **Download Model Files**: After saving the model files to Google Drive, you can download them to your local machine. Go to your Google Drive, locate the model directory, and download the files.
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5. **Load Model Locally**: Once you have downloaded the model files to your local machine, you can load the model using the `from_pretrained` method.
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```python
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from transformers import AutoModel, AutoTokenizer
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model = AutoModel.from_pretrained('path_to_your_model_directory')
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tokenizer = AutoTokenizer.from_pretrained('path_to_your_model_directory')
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```
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By following these steps, you can easily export a fine-tuned model from Colab and use it locally on your machine.
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## 🦥 Unsloth.ai News
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- 📣 NEW! Introducing [Reasoning](https://unsloth.ai/blog/r1-reasoning) in Unsloth. You can now reproduce DeepSeek-R1's "aha" moment with just 7GB VRAM. Transform Llama, Phi, Mistral etc. into reasoning LLMs!
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- 📣 NEW! [DeepSeek-R1](https://unsloth.ai/blog/deepseek-r1) - the most powerful open reasoning models with Llama & Qwen distillations. Run or fine-tune them now! More details: [unsloth.ai/blog/deepseek-r1](https://unsloth.ai/blog/deepseek-r1). All model uploads: [here](https://huggingface.co/collections/unsloth/deepseek-r1-all-versions-678e1c48f5d2fce87892ace5).
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@ -44,6 +44,7 @@ __all__ = [
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"save_to_gguf",
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"patch_saving_functions",
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"create_huggingface_repo",
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"export_model_to_local",
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]
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# llama.cpp specific targets - all takes 90s. Below takes 60s
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@ -1025,69 +1026,6 @@ def save_to_gguf(
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)
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pass
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# Determine whether the system already has llama.cpp installed and the scripts are executable
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quantize_location = get_executable(["llama-quantize", "quantize"])
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convert_location = get_executable(["convert-hf-to-gguf.py", "convert_hf_to_gguf.py"])
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error = 0
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if quantize_location is not None and convert_location is not None:
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print("Unsloth: llama.cpp found in the system. We shall skip installation.")
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else:
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print("Unsloth: Installing llama.cpp. This might take 3 minutes...")
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if _run_installer is not None:
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_run_installer, IS_CMAKE = _run_installer
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error = _run_installer.wait()
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# Check if successful
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if error != 0:
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print(f"Unsloth: llama.cpp error code = {error}.")
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install_llama_cpp_old(-10)
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pass
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if IS_CMAKE:
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# CMAKE needs to do some extra steps
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print("Unsloth: CMAKE detected. Finalizing some steps for installation.")
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check = os.system("cp llama.cpp/build/bin/llama-* llama.cpp")
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if check != 0: raise RuntimeError("Failed compiling llama.cpp. Please report this ASAP!")
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check = os.system("rm -rf llama.cpp/build")
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if check != 0: raise RuntimeError("Failed compiling llama.cpp. Please report this ASAP!")
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pass
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else:
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error = 0
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install_llama_cpp_blocking()
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pass
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# Careful llama.cpp/quantize changed to llama.cpp/llama-quantize
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# and llama.cpp/main changed to llama.cpp/llama-cli
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# See https://github.com/ggerganov/llama.cpp/pull/7809
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quantize_location = None
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if os.path.exists("llama.cpp/quantize"):
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quantize_location = "llama.cpp/quantize"
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elif os.path.exists("llama.cpp/llama-quantize"):
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quantize_location = "llama.cpp/llama-quantize"
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else:
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raise RuntimeError(
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"Unsloth: The file 'llama.cpp/llama-quantize' or 'llama.cpp/quantize' does not exist.\n"\
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"But we expect this file to exist! Maybe the llama.cpp developers changed the name?"
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)
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pass
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# See https://github.com/unslothai/unsloth/pull/730
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# Filenames changed again!
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convert_location = None
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if os.path.exists("llama.cpp/convert-hf-to-gguf.py"):
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convert_location = "llama.cpp/convert-hf-to-gguf.py"
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elif os.path.exists("llama.cpp/convert_hf_to_gguf.py"):
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convert_location = "llama.cpp/convert_hf_to_gguf.py"
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else:
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raise RuntimeError(
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"Unsloth: The file 'llama.cpp/convert-hf-to-gguf.py' or 'llama.cpp/convert_hf_to_gguf.py' does not exist.\n"\
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"But we expect this file to exist! Maybe the llama.cpp developers changed the name?"
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)
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pass
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pass
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# Determine maximum first_conversion state
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if first_conversion == "f32" : strength = 3
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elif first_conversion == "f16" : strength = 2
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@ -1315,7 +1253,7 @@ def unsloth_push_to_hub_merged(
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safe_serialization : bool = True,
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revision : str = None,
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commit_description : str = "Upload model trained with Unsloth 2x faster",
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tags : Optional[List[str]] = None,
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tags : List[str] = None,
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temporary_location : str = "_unsloth_temporary_saved_buffers",
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maximum_memory_usage : float = 0.75,
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):
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@ -1823,6 +1761,8 @@ def unsloth_push_to_hub_gguf(
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"q4_0" : "Original quant method, 4-bit.",
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"q4_1" : "Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.",
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"q4_k_s" : "Uses Q4_K for all tensors",
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"q4_k" : "alias for q4_k_m",
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"q5_k" : "alias for q5_k_m",
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"q5_0" : "Higher accuracy, higher resource usage and slower inference.",
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"q5_1" : "Even higher accuracy, resource usage and slower inference.",
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"q5_k_s" : "Uses Q5_K for all tensors",
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@ -2347,3 +2287,27 @@ def patch_saving_functions(model, vision = False):
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pass
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return model
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pass
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def export_model_to_local(model, tokenizer, save_directory, drive_directory):
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"""
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Export a fine-tuned model from Colab to your local machine.
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Args:
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model: The fine-tuned model to be exported.
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tokenizer: The tokenizer associated with the model.
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save_directory: The directory where the model will be saved in Colab.
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drive_directory: The directory in Google Drive where the model will be saved.
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"""
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# Save the model in Colab
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model.save_pretrained(save_directory)
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tokenizer.save_pretrained(save_directory)
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# Mount Google Drive
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from google.colab import drive
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drive.mount('/content/drive')
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# Copy the model files to Google Drive
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import shutil
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shutil.copytree(save_directory, drive_directory)
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print(f"Model saved to {drive_directory} in Google Drive. You can now download it to your local machine.")
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