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--- |
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license: other |
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language: |
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- en |
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pipeline_tag: text-generation |
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inference: false |
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tags: |
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- transformers |
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- gguf |
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- imatrix |
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- SmolLM2-1.7B-Instruct |
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--- |
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Quantizations of https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct |
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### Inference Clients/UIs |
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* [llama.cpp](https://github.com/ggerganov/llama.cpp) |
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* [KoboldCPP](https://github.com/LostRuins/koboldcpp) |
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* [ollama](https://github.com/ollama/ollama) |
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* [text-generation-webui](https://github.com/oobabooga/text-generation-webui) |
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* [GPT4All](https://github.com/nomic-ai/gpt4all) |
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* [jan](https://github.com/janhq/jan) |
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--- |
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# From original readme |
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SmolLM2 is a family of compact language models available in three size: 135M, 360M, and 1.7B parameters. They are capable of solving a wide range of tasks while being lightweight enough to run on-device. |
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The 1.7B variant demonstrates significant advances over its predecessor SmolLM1-1.7B, particularly in instruction following, knowledge, reasoning, and mathematics. It was trained on 11 trillion tokens using a diverse dataset combination: FineWeb-Edu, DCLM, The Stack, along with new mathematics and coding datasets that we curated and will release soon. We developed the instruct version through supervised fine-tuning (SFT) using a combination of public datasets and our own curated datasets. We then applied Direct Preference Optimization (DPO) using [UltraFeedback](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized). |
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The instruct model additionally supports tasks such as text rewriting, summarization and function calling thanks to datasets developed by [Argilla](https://huggingface.co/argilla) such as [Synth-APIGen-v0.1](https://huggingface.co/datasets/argilla/Synth-APIGen-v0.1). |
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### How to use |
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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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from transformers import AutoModelForCausalLM, AutoTokenizer |
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checkpoint = "HuggingFaceTB/SmolLM2-1.7B-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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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 ch |
