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--- |
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license: apache-2.0 |
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base_model: HuggingFaceTB/SmolLM-135M-Instruct |
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tags: |
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- alignment-handbook |
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- trl |
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- unsloth |
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datasets: |
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- Magpie-Align/Magpie-Pro-300K-Filtered |
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- bigcode/self-oss-instruct-sc2-exec-filter-50k |
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- teknium/OpenHermes-2.5 |
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- HuggingFaceTB/everyday-conversations-llama3.1-2k |
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library_name: transformers |
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language: |
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- en |
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--- |
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# Finetune Llama 3.1, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth! |
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We have a free Google Colab Tesla T4 notebook for Llama 3.1 (8B) here - also works for SmolLM!: https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing |
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/unsloth) |
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) |
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## ✨ Finetune for Free |
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All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face. |
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| Unsloth supports | Free Notebooks | Performance | Memory use | |
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|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------| |
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| **Llama-3.1 8b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing) | 2.4x faster | 58% less | |
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| **Phi-3.5 (mini)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1lN6hPQveB_mHSnTOYifygFcrO8C1bxq4?usp=sharing) | 2x faster | 50% less | |
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| **Gemma-2 9b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing) | 2.4x faster | 58% less | |
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| **Mistral 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing) | 2.2x faster | 62% less | |
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| **TinyLlama** | [▶️ Start on Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing) | 3.9x faster | 74% less | |
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| **DPO - Zephyr** | [▶️ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) | 1.9x faster | 19% less | |
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- This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates. |
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- This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr. |
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- \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster. |
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# SmolLM-1.7B-Instruct |
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<center> |
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<img src="https://huggingface.co./datasets/HuggingFaceTB/images/resolve/main/banner_smol.png" alt="SmolLM" width="1100" height="600"> |
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</center> |
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## Model Summary |
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SmolLM is a series of small language models available in three sizes: 135M, 360M, and 1.7B parameters. |
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These models are pre-trained on [SmolLM-Corpus](https://huggingface.co./datasets/HuggingFaceTB/smollm-corpus), a curated collection of high-quality educational and synthetic data designed for training LLMs. For further details, we refer to our [blogpost](https://huggingface.co./blog/smollm). |
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To build SmolLM-Instruct, we finetuned the base models on publicly available datasets. |
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## Changelog |
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|Release|Description| |
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|v0.1| Initial release of SmolLM-Instruct. We finetune on the permissive subset of the [WebInstructSub](https://huggingface.co./datasets/TIGER-Lab/WebInstructSub) dataset, combined with [StarCoder2-Self-OSS-Instruct](https://huggingface.co./datasets/bigcode/self-oss-instruct-sc2-exec-filter-50k). Then, we perform DPO (Direct Preference Optimization) for one epoch on [HelpSteer](https://huggingface.co./datasets/nvidia/HelpSteer) for the 135M and 1.7B models, and [argilla/dpo-mix-7k](https://huggingface.co./datasets/argilla/dpo-mix-7k) for the 360M model.| |
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|v0.2| We changed the finetuning mix to datasets more suitable for smol models. We train on a new dataset of 2k simple everyday conversations we generated by llama3.1-70B [everyday-conversations-llama3.1-2k](https://huggingface.co./datasets/HuggingFaceTB/everyday-conversations-llama3.1-2k/), [Magpie-Pro-300K-Filtered](https://huggingface.co./datasets/Magpie-Align/Magpie-Pro-300K-Filtered), [StarCoder2-Self-OSS-Instruct](https://huggingface.co./datasets/bigcode/self-oss-instruct-sc2-exec-filter-50k), and a small subset of [OpenHermes-2.5](https://huggingface.co./datasets/teknium/OpenHermes-2.5)| |
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v0.2 models are better at staying on topic and responding appropriately to standard prompts, such as greetings and questions about their role as AI assistants. SmolLM-360M-Instruct (v0.2) has a 63.3% win rate over SmolLM-360M-Instruct (v0.1) on AlpacaEval. You can find the details [here](https://huggingface.co./datasets/HuggingFaceTB/alpaca_eval_details/). |
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You can load v0.1 checkpoint by specifying `revision="v0.1"` in the transformers code: |
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```python |
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model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM-1.7B-Instruct", revision="v0.1") |
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``` |
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## Usage |
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### Local Applications |
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⚡ For local applications, you can find optimized implementations of the model in MLC, GGUF and Transformers.js formats, in addition to fast in-browser demos in this collection: https://huggingface.co./collections/HuggingFaceTB/local-smollms-66c0f3b2a15b4eed7fb198d0 |
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We noticed that 4bit quantization degrades the quality of the 135M and 360M, so we use `q016` for MLC and ONNX/Transformers.js checkpoints for the WebGPU demos. We also suggest using temperature 0.2 and top-p 0.9. |
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### Transformers |
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```bash |
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pip install transformers |
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``` |
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```python |
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# pip install transformers |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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checkpoint = "HuggingFaceTB/SmolLM-1.7B-Instruct" |
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device = "cuda" # for GPU usage or "cpu" for CPU usage |
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tokenizer = AutoTokenizer.from_pretrained(checkpoint) |
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# for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")` |
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model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device) |
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messages = [{"role": "user", "content": "What is the capital of France."}] |
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input_text=tokenizer.apply_chat_template(messages, tokenize=False) |
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print(input_text) |
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inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) |
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outputs = model.generate(inputs, max_new_tokens=50, temperature=0.2, top_p=0.9, do_sample=True) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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### Chat in TRL |
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You can also use the TRL CLI to chat with the model from the terminal: |
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```bash |
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pip install trl |
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trl chat --model_name_or_path HuggingFaceTB/SmolLM-1.7B-Instruct --device cpu |
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``` |
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## Limitations |
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Additionally, the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data, we invite users to leverage them as assistive tools rather than definitive sources of information. We find that they can handle general knowledge questions, creative writing and basic Python programming. But they are English only and may have difficulty with arithmetics, editing tasks and complex reasoning. For more details about the models' capabilities, please refer to our [blog post](https://huggingface.co./blog/smollm). |
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## Training parameters |
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We train the models using the [alignment-handbook](https://github.com/huggingface/alignment-handbook) with the datasets mentioned in the changelog, using these parameters v0.2 (most of them are from Zephyr Gemma recipe): |
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- 1 epoch |
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- lr 1e-3 |
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- cosine schedule |
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- warmup ratio 0.1 |
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- global batch size 262k tokens |
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You can find the training recipe here: https://github.com/huggingface/alignment-handbook/tree/smollm/recipes/smollm |
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# Citation |
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```bash |
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@misc{allal2024SmolLM, |
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title={SmolLM - blazingly fast and remarkably powerful}, |
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author={Loubna Ben Allal and Anton Lozhkov and Elie Bakouch and Leandro von Werra and Thomas Wolf}, |
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year={2024}, |
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} |
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``` |
