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README.md

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+---
+license: other
+language:
+- en
+pipeline_tag: text-generation
+inference: false
+tags:
+- transformers
+- gguf
+- imatrix
+- SmolLM2-1.7B
+---
+Quantizations of https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B
+
+
+### Inference Clients/UIs
+* [llama.cpp](https://github.com/ggerganov/llama.cpp)
+* [KoboldCPP](https://github.com/LostRuins/koboldcpp)
+* [ollama](https://github.com/ollama/ollama)
+* [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
+* [GPT4All](https://github.com/nomic-ai/gpt4all)
+* [jan](https://github.com/janhq/jan)
+---
+
+# From original readme
+
+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.
+
+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).
+
+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).
+
+### How to use
+
+```bash
+pip install transformers
+```
+
+#### Running the model on CPU/GPU/multi GPU
+* _Using full precision_
+```python
+# pip install transformers
+from transformers import AutoModelForCausalLM, AutoTokenizer
+checkpoint = "HuggingFaceTB/SmolLM2-1.7B"
+device = "cuda" # for GPU usage or "cpu" for CPU usage
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+# for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")`
+model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
+inputs = tokenizer.encode("Gravity is", return_tensors="pt").to(device)
+outputs = model.generate(inputs)
+print(tokenizer.decode(outputs[0]))
+```
+
+* _Using `torch.bfloat16`_
+```python
+# pip install accelerate
+# for fp16 use `torch_dtype=torch.float16` instead
+model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", torch_dtype=torch.bfloat16)
+inputs = tokenizer.encode("Gravity is", return_tensors="pt").to("cuda")
+outputs = model.generate(inputs)
+print(tokenizer.decode(outputs[0]))
+```
+```bash
+>>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB")
+Memory footprint: 3422.76 MB
+```