Text Generation
Transformers
TensorBoard
ONNX
Safetensors
Transformers.js
English
llama
conversational
text-generation-inference
Instructions to use viktor-shcherb/SmolLM2-360M-Tools with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use viktor-shcherb/SmolLM2-360M-Tools with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="viktor-shcherb/SmolLM2-360M-Tools") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("viktor-shcherb/SmolLM2-360M-Tools") model = AutoModelForCausalLM.from_pretrained("viktor-shcherb/SmolLM2-360M-Tools") 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]:])) - Transformers.js
How to use viktor-shcherb/SmolLM2-360M-Tools with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('text-generation', 'viktor-shcherb/SmolLM2-360M-Tools'); - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use viktor-shcherb/SmolLM2-360M-Tools with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "viktor-shcherb/SmolLM2-360M-Tools" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "viktor-shcherb/SmolLM2-360M-Tools", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/viktor-shcherb/SmolLM2-360M-Tools
- SGLang
How to use viktor-shcherb/SmolLM2-360M-Tools 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 "viktor-shcherb/SmolLM2-360M-Tools" \ --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": "viktor-shcherb/SmolLM2-360M-Tools", "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 "viktor-shcherb/SmolLM2-360M-Tools" \ --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": "viktor-shcherb/SmolLM2-360M-Tools", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use viktor-shcherb/SmolLM2-360M-Tools with Docker Model Runner:
docker model run hf.co/viktor-shcherb/SmolLM2-360M-Tools
File size: 6,018 Bytes
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"bos_token": "<|im_start|>",
"chat_template": "{%- set first_is_system = (messages and messages[0]['role'] == 'system') -%}\n\n{# ---------- SYSTEM MESSAGE WITH TOOL INSTRUCTIONS ---------- #}\n<|im_start|>system\n{%- if first_is_system -%}\n{{ messages[0]['content'] }}\n{%- else -%}\nYou are a helpful AI assistant named SmolLM, trained by Hugging Face.\n{%- endif %}\n\nYou can call external tools (\"functions\") to get real-time data or take actions for the user.\n\nTOOL CALL PROTOCOL:\n1. When you decide a tool is needed, respond with ONLY one or more <tool_call>...</tool_call> blocks.\n Do not include anything else in that turn.\n2. Each <tool_call> block MUST contain valid JSON with this shape:\n {\n \"id\": \"<unique_call_id>\",\n \"type\": \"function\",\n \"function\": {\n \"name\": \"<tool_name>\",\n \"arguments\": { ... }\n }\n }\n \"arguments\" MUST be valid JSON. Use the correct parameter names and types.\n3. After the tool(s) run, you will receive one or more <tool_response>...</tool_response> messages.\n Call another tool or respond as assistant, using the tool results.\n4. If no tool is required, answer directly in natural language.\n\nAVAILABLE TOOLS:\n{%- if tools is defined and tools|length > 0 %}\n{%- for t in tools %}\n- {{ t[\"function\"][\"name\"] }} :\n {{ t[\"function\"][\"description\"] }}\n params: {{ t[\"function\"][\"parameters\"] | tojson }}\n{%- endfor %}\n{%- else %}\n(no tools provided)\n{%- endif %}\n\nYou must follow the protocol exactly.\n<|im_end|>\n\n{# ---------- REST OF MESSAGES ---------- #}\n{%- for m in (messages[1:] if first_is_system else messages) %}\n\n{# Assistant turn that issues tool calls #}\n{%- if m['role'] == 'assistant' and ('tool_calls' in m) and m['tool_calls'] %}\n<|im_start|>assistant\n{%- for c in m['tool_calls'] %}\n<tool_call>{{ {\n \"id\": c[\"id\"],\n \"type\": c.get(\"type\", \"function\"),\n \"function\": {\n \"name\": c[\"function\"][\"name\"],\n \"arguments\": (\n c[\"function\"][\"arguments\"]\n if c[\"function\"][\"arguments\"] is not none\n else {}\n )\n }\n} | tojson }}</tool_call>\n{%- endfor %}\n<|im_end|>\n\n{# Tool result turn #}\n{%- elif m['role'] == 'tool' %}\n<|im_start|>user\n<tool_response{% if m.get('tool_call_id') %} id=\"{{ m['tool_call_id'] }}\"{% endif %}>\n{{ m['content'] }}\n</tool_response>\n<|im_end|>\n\n{# Normal assistant / user / other turns #}\n{%- else %}\n<|im_start|>{{ m['role'] }}\n{{ m['content'] }}\n<|im_end|>\n{%- endif %}\n\n{%- endfor %}\n\n{# ---------- GENERATION PROMPT ---------- #}\n{%- if add_generation_prompt %}\n<|im_start|>assistant\n{%- endif %}\n",
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"model_max_length": 8192,
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"tokenizer_class": "GPT2Tokenizer",
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"vocab_size": 49152
} |