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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 |
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--- |
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Quantizations of https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B |
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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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```bash |
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pip install transformers |
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``` |
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#### Running the model on CPU/GPU/multi GPU |
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* _Using full precision_ |
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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/SmolLM2-1.7B" |
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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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inputs = tokenizer.encode("Gravity is", return_tensors="pt").to(device) |
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outputs = model.generate(inputs) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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* _Using `torch.bfloat16`_ |
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```python |
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# pip install accelerate |
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# for fp16 use `torch_dtype=torch.float16` instead |
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model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", torch_dtype=torch.bfloat16) |
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inputs = tokenizer.encode("Gravity is", return_tensors="pt").to("cuda") |
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outputs = model.generate(inputs) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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```bash |
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>>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB") |
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Memory footprint: 3422.76 MB |
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``` |
