Text Generation
Transformers
Safetensors
English
llama
unsloth
conversational
text-generation-inference
Instructions to use unsloth/SmolLM2-360M-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unsloth/SmolLM2-360M-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="unsloth/SmolLM2-360M-Instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("unsloth/SmolLM2-360M-Instruct") model = AutoModelForCausalLM.from_pretrained("unsloth/SmolLM2-360M-Instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use unsloth/SmolLM2-360M-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "unsloth/SmolLM2-360M-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "unsloth/SmolLM2-360M-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/unsloth/SmolLM2-360M-Instruct
- SGLang
How to use unsloth/SmolLM2-360M-Instruct with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "unsloth/SmolLM2-360M-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "unsloth/SmolLM2-360M-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "unsloth/SmolLM2-360M-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "unsloth/SmolLM2-360M-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio
How to use unsloth/SmolLM2-360M-Instruct with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for unsloth/SmolLM2-360M-Instruct to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for unsloth/SmolLM2-360M-Instruct to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for unsloth/SmolLM2-360M-Instruct to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="unsloth/SmolLM2-360M-Instruct", max_seq_length=2048, ) - Docker Model Runner
How to use unsloth/SmolLM2-360M-Instruct with Docker Model Runner:
docker model run hf.co/unsloth/SmolLM2-360M-Instruct
Upload tokenizer
Browse files- added_tokens.json +3 -0
- special_tokens_map.json +2 -8
- tokenizer.json +9 -0
- tokenizer_config.json +10 -2
added_tokens.json
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{
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"<|PAD_TOKEN|>": 49152
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}
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special_tokens_map.json
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"rstrip": false,
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"single_word": false
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},
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"pad_token": "<|
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"unk_token":
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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"rstrip": false,
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"single_word": false
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},
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"pad_token": "<|PAD_TOKEN|>",
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"unk_token": "�"
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}
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tokenizer.json
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"rstrip": false,
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"normalized": false,
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"special": true
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],
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"normalizer": null,
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"rstrip": false,
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"normalized": false,
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"special": true
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},
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{
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"id": 49152,
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"content": "<|PAD_TOKEN|>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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"special": true
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],
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"normalizer": null,
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tokenizer_config.json
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"additional_special_tokens": [
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"model_max_length": 2048,
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"pad_token": "<|
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"padding_side": "left",
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"tokenizer_class": "GPT2Tokenizer",
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"unk_token": "
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"vocab_size": 49152
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}
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"49152": {
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"content": "<|PAD_TOKEN|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"additional_special_tokens": [
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"model_max_length": 2048,
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"pad_token": "<|PAD_TOKEN|>",
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"padding_side": "left",
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"tokenizer_class": "GPT2Tokenizer",
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"unk_token": "�",
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"vocab_size": 49152
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}
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