--- license: other language: - en pipeline_tag: text-generation inference: false tags: - transformers - gguf - imatrix - SmolLM2-1.7B-Instruct --- Quantizations of https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct ### 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 ### Transformers ```bash pip install transformers ``` ```python from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "HuggingFaceTB/SmolLM2-1.7B-Instruct" 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) messages = [{"role": "user", "content": "What is the capital of France."}] input_text=tokenizer.apply_chat_template(messages, tokenize=False) inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) outputs = model.generate(inputs, max_new_tokens=50, temperature=0.2, top_p=0.9, do_sample=True) print(tokenizer.decode(outputs[0])) ``` ### Chat in TRL You can also use the TRL CLI to chat with the model from the terminal: ```bash pip install trl trl ch