Text Generation
Transformers
Safetensors
GGUF
English
smollm3
formal-logic
reasoning
lora
model-merging
wise-ft
reinforcement-learning
grpo
twil-lm
conversational
Instructions to use webAI-Official/TwIL-LM3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use webAI-Official/TwIL-LM3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="webAI-Official/TwIL-LM3") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("webAI-Official/TwIL-LM3") model = AutoModelForCausalLM.from_pretrained("webAI-Official/TwIL-LM3", device_map="auto") 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
- llama.cpp
How to use webAI-Official/TwIL-LM3 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf webAI-Official/TwIL-LM3:Q4_K_M # Run inference directly in the terminal: llama cli -hf webAI-Official/TwIL-LM3:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf webAI-Official/TwIL-LM3:Q4_K_M # Run inference directly in the terminal: llama cli -hf webAI-Official/TwIL-LM3:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf webAI-Official/TwIL-LM3:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf webAI-Official/TwIL-LM3:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf webAI-Official/TwIL-LM3:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf webAI-Official/TwIL-LM3:Q4_K_M
Use Docker
docker model run hf.co/webAI-Official/TwIL-LM3:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use webAI-Official/TwIL-LM3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "webAI-Official/TwIL-LM3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "webAI-Official/TwIL-LM3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/webAI-Official/TwIL-LM3:Q4_K_M
- SGLang
How to use webAI-Official/TwIL-LM3 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 "webAI-Official/TwIL-LM3" \ --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": "webAI-Official/TwIL-LM3", "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 "webAI-Official/TwIL-LM3" \ --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": "webAI-Official/TwIL-LM3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use webAI-Official/TwIL-LM3 with Ollama:
ollama run hf.co/webAI-Official/TwIL-LM3:Q4_K_M
- Unsloth Studio
How to use webAI-Official/TwIL-LM3 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 webAI-Official/TwIL-LM3 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 webAI-Official/TwIL-LM3 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for webAI-Official/TwIL-LM3 to start chatting
- Pi
How to use webAI-Official/TwIL-LM3 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf webAI-Official/TwIL-LM3:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "webAI-Official/TwIL-LM3:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use webAI-Official/TwIL-LM3 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf webAI-Official/TwIL-LM3:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "webAI-Official/TwIL-LM3:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use webAI-Official/TwIL-LM3 with Docker Model Runner:
docker model run hf.co/webAI-Official/TwIL-LM3:Q4_K_M
- Lemonade
How to use webAI-Official/TwIL-LM3 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull webAI-Official/TwIL-LM3:Q4_K_M
Run and chat with the model
lemonade run user.TwIL-LM3-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use webAI-Official/TwIL-LM3 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf webAI-Official/TwIL-LM3:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default webAI-Official/TwIL-LM3:Q4_K_M
Run Hermes
hermes
- Atomic Chat
| {# ───── defaults ───── #} | |
| {%- if enable_thinking is not defined -%} | |
| {%- set enable_thinking = true -%} | |
| {%- endif -%} | |
| {# ───── reasoning mode ───── #} | |
| {%- if enable_thinking -%} | |
| {%- set reasoning_mode = "/think" -%} | |
| {%- else -%} | |
| {%- set reasoning_mode = "/no_think" -%} | |
| {%- endif -%} | |
| {# ───── header (system message) ───── #} | |
| {{- "<|im_start|>system\n" -}} | |
| {%- if messages[0].role == "system" -%} | |
| {%- set system_message = messages[0].content -%} | |
| {%- if "/no_think" in system_message -%} | |
| {%- set reasoning_mode = "/no_think" -%} | |
| {%- elif "/think" in system_message -%} | |
| {%- set reasoning_mode = "/think" -%} | |
| {%- endif -%} | |
| {%- set custom_instructions = system_message.replace("/no_think", "").replace("/think", "").rstrip() -%} | |
| {%- endif -%} | |
| {%- if "/system_override" in system_message -%} | |
| {{- custom_instructions.replace("/system_override", "").rstrip() -}} | |
| {{- "<|im_end|>\n" -}} | |
| {%- else -%} | |
| {{- "## Metadata\n\n" -}} | |
| {{- "Knowledge Cutoff Date: June 2025\n" -}} | |
| {%- set today = strftime_now("%d %B %Y") -%} | |
| {{- "Today Date: " ~ today ~ "\n" -}} | |
| {{- "Reasoning Mode: " + reasoning_mode + "\n\n" -}} | |
| {{- "## Custom Instructions\n\n" -}} | |
| {%- if custom_instructions -%} | |
| {{- custom_instructions + "\n\n" -}} | |
| {%- elif reasoning_mode == "/think" -%} | |
| {{- "You are a helpful AI assistant named SmolLM, trained by Hugging Face. Your role as an assistant involves thoroughly exploring questions through a systematic thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracking, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution using the specified format: <think> Thought section </think> Solution section. In the Thought section, detail your reasoning process in steps. Each step should include detailed considerations such as analysing questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion.\n\n" -}} | |
| {%- else -%} | |
| {{- "You are a helpful AI assistant named SmolLM, trained by Hugging Face.\n\n" -}} | |
| {%- endif -%} | |
| {%- if xml_tools or python_tools or tools -%} | |
| {{- "### Tools\n\n" -}} | |
| {%- if xml_tools or tools -%} | |
| {%- if tools -%} | |
| {%- set xml_tools = tools -%} | |
| {%- endif -%} | |
| {%- set ns = namespace(xml_tool_string="You may call one or more functions to assist with the user query.\nYou are provided with function signatures within <tools></tools> XML tags:\n\n<tools>\n") -%} | |
| {%- for tool in xml_tools[:] -%} {# The slicing makes sure that xml_tools is a list #} | |
| {%- set ns.xml_tool_string = ns.xml_tool_string ~ (tool | string) ~ "\n" -%} | |
| {%- endfor -%} | |
| {%- set xml_tool_string = ns.xml_tool_string + "</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call>" -%} | |
| {{- xml_tool_string -}} | |
| {%- endif -%} | |
| {%- if python_tools -%} | |
| {%- set ns = namespace(python_tool_string="When you send a message containing Python code between '<code>' and '</code>' tags, it will be executed in a stateful Jupyter notebook environment, and you will then be given the output to continued reasoning in an agentic loop.\n\nYou can use the following tools in your python code like regular functions:\n<tools>\n") -%} | |
| {%- for tool in python_tools[:] -%} {# The slicing makes sure that python_tools is a list #} | |
| {%- set ns.python_tool_string = ns.python_tool_string ~ (tool | string) ~ "\n" -%} | |
| {%- endfor -%} | |
| {%- set python_tool_string = ns.python_tool_string + "</tools>\n\nThe state persists between code executions: so variables that you define in one step are still available thereafter." -%} | |
| {{- python_tool_string -}} | |
| {%- endif -%} | |
| {{- "\n\n" -}} | |
| {{- "<|im_end|>\n" -}} | |
| {%- endif -%} | |
| {%- endif -%} | |
| {# ───── main loop ───── #} | |
| {%- for message in messages -%} | |
| {%- set content = message.content if message.content is string else "" -%} | |
| {%- if message.role == "user" -%} | |
| {{ "<|im_start|>" + message.role + "\n" + content + "<|im_end|>\n" }} | |
| {%- elif message.role == "assistant" -%} | |
| {% generation %} | |
| {%- if reasoning_mode == "/think" -%} | |
| {{ "<|im_start|>assistant\n" + content.lstrip("\n") + "<|im_end|>\n" }} | |
| {%- else -%} | |
| {{ "<|im_start|>assistant\n" + "<think>\n\n</think>\n" + content.lstrip("\n") + "<|im_end|>\n" }} | |
| {%- endif -%} | |
| {% endgeneration %} | |
| {%- elif message.role == "tool" -%} | |
| {{ "<|im_start|>" + "user\n" + content + "<|im_end|>\n" }} | |
| {%- endif -%} | |
| {%- endfor -%} | |
| {# ───── generation prompt ───── #} | |
| {%- if add_generation_prompt -%} | |
| {%- if reasoning_mode == "/think" -%} | |
| {{ "<|im_start|>assistant\n" }} | |
| {%- else -%} | |
| {{ "<|im_start|>assistant\n" + "<think>\n\n</think>\n" }} | |
| {%- endif -%} | |
| {%- endif -%} |