Kaggle All MiniLM L6 V2
To run this, press "Runtime" and press "Run all" on a free Tesla T4 Google Colab instance!
To install Unsloth on your local device, follow our guide. This notebook is licensed LGPL-3.0.
You will learn how to do data prep, how to train, how to run the model, & how to save it
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Installation
Unsloth
🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning. 🦥 Unsloth Zoo will now patch everything to make training faster!
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Unsloth: Using fast encoder path for bert (torch.compile + SDPA)
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`torch_dtype` is deprecated! Use `dtype` instead!
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We now add LoRA adapters so we only need to update a small amount of parameters!
Unsloth: torch.compile will be applied automatically if max_steps > 2616
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Let's take a look at the dataset structure:
Dataset examples:
{'anchor': 'A person on a horse jumps over a broken down airplane.', 'positive': 'A person is outdoors, on a horse.'}
{'anchor': 'Children smiling and waving at camera', 'positive': 'There are children present'}
{'anchor': 'A boy is jumping on skateboard in the middle of a red bridge.', 'positive': 'The boy does a skateboarding trick.'}
{'anchor': 'Two blond women are hugging one another.', 'positive': 'There are women showing affection.'}
{'anchor': 'A few people in a restaurant setting, one of them is drinking orange juice.', 'positive': 'The diners are at a restaurant.'}
{'anchor': 'An older man is drinking orange juice at a restaurant.', 'positive': 'A man is drinking juice.'}
Baseline Inference
Let's test the base model before fine-tuning to see how it performs on our specific domain.
--- Pre-Training Results for query: 'A soccer player in a blue jersey is running across the field.' --- 0.4980 | The player is about to score a goal for his team. 0.4700 | An athlete is practicing a sport outdoors. 0.3125 | Jersey is a knit fabric used predominantly for clothing manufacture. 0.0739 | A person is sitting quietly on the grass.
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GPU = Tesla T4. Max memory = 14.741 GB. 0.078 GB of memory reserved.
Let's train the model! To resume a training run, set trainer.train(resume_from_checkpoint = True)
230.1043 seconds used for training. 3.84 minutes used for training. Peak reserved memory = 3.287 GB. Peak reserved memory for training = 3.209 GB. Peak reserved memory % of max memory = 22.298 %. Peak reserved memory for training % of max memory = 21.769 %.
--- Post-Training Results for query: 'A soccer player in a blue jersey is running across the field.' --- 0.4459 | An athlete is practicing a sport outdoors. 0.4322 | The player is about to score a goal for his team. 0.3113 | Jersey is a knit fabric used predominantly for clothing manufacture. 0.0944 | A person is sitting quietly on the grass.
Now if you want to load the LoRA adapters we just saved for inference, set False to True:
Saving to float16 for VLLM
We also support saving to float16 directly. Select merged_16bit for float16 or merged_4bit for int4. We also allow lora adapters as a fallback. Use push_to_hub_merged to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens. See our docs for more deployment options.
GGUF / llama.cpp Conversion
To save to GGUF / llama.cpp, we support it natively now! We clone llama.cpp and we default save it to q8_0. We allow all methods like q4_k_m. Use save_pretrained_gguf for local saving and push_to_hub_gguf for uploading to HF.
Some supported quant methods (full list on our docs page):
q8_0- Fast conversion. High resource use, but generally acceptable.q4_k_m- Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.q5_k_m- Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K.
And we're done! If you have any questions on Unsloth, we have a Discord 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!
Some other resources:
- Looking to use Unsloth locally? Read our Installation Guide for details on installing Unsloth on Windows, Docker, AMD, Intel GPUs.
- Learn how to do Reinforcement Learning with our RL Guide and notebooks.
- Read our guides and notebooks for Text-to-speech (TTS) and vision model support.
- Explore our LLM Tutorials Directory to find dedicated guides for each model.
- Need help with Inference? Read our Inference & Deployment page for details on using vLLM, llama.cpp, Ollama etc.
