Text Generation
Transformers
Safetensors
GGUF
English
lfm2
text-generation-inference
unsloth
lfm2.5
code
reasoning
conversational
Instructions to use Schnuckade/LFM-2.5-Coder-2.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Schnuckade/LFM-2.5-Coder-2.6B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Schnuckade/LFM-2.5-Coder-2.6B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Schnuckade/LFM-2.5-Coder-2.6B") model = AutoModelForCausalLM.from_pretrained("Schnuckade/LFM-2.5-Coder-2.6B", 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 Schnuckade/LFM-2.5-Coder-2.6B 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 Schnuckade/LFM-2.5-Coder-2.6B:Q4_K_M # Run inference directly in the terminal: llama cli -hf Schnuckade/LFM-2.5-Coder-2.6B:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Schnuckade/LFM-2.5-Coder-2.6B:Q4_K_M # Run inference directly in the terminal: llama cli -hf Schnuckade/LFM-2.5-Coder-2.6B: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 Schnuckade/LFM-2.5-Coder-2.6B:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Schnuckade/LFM-2.5-Coder-2.6B: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 Schnuckade/LFM-2.5-Coder-2.6B:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Schnuckade/LFM-2.5-Coder-2.6B:Q4_K_M
Use Docker
docker model run hf.co/Schnuckade/LFM-2.5-Coder-2.6B:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Schnuckade/LFM-2.5-Coder-2.6B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Schnuckade/LFM-2.5-Coder-2.6B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Schnuckade/LFM-2.5-Coder-2.6B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Schnuckade/LFM-2.5-Coder-2.6B:Q4_K_M
- SGLang
How to use Schnuckade/LFM-2.5-Coder-2.6B 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 "Schnuckade/LFM-2.5-Coder-2.6B" \ --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": "Schnuckade/LFM-2.5-Coder-2.6B", "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 "Schnuckade/LFM-2.5-Coder-2.6B" \ --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": "Schnuckade/LFM-2.5-Coder-2.6B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use Schnuckade/LFM-2.5-Coder-2.6B with Ollama:
ollama run hf.co/Schnuckade/LFM-2.5-Coder-2.6B:Q4_K_M
- Unsloth Studio
How to use Schnuckade/LFM-2.5-Coder-2.6B 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 Schnuckade/LFM-2.5-Coder-2.6B 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 Schnuckade/LFM-2.5-Coder-2.6B to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Schnuckade/LFM-2.5-Coder-2.6B to start chatting
- Pi
How to use Schnuckade/LFM-2.5-Coder-2.6B with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Schnuckade/LFM-2.5-Coder-2.6B: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": "Schnuckade/LFM-2.5-Coder-2.6B:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Schnuckade/LFM-2.5-Coder-2.6B with Docker Model Runner:
docker model run hf.co/Schnuckade/LFM-2.5-Coder-2.6B:Q4_K_M
- Lemonade
How to use Schnuckade/LFM-2.5-Coder-2.6B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Schnuckade/LFM-2.5-Coder-2.6B:Q4_K_M
Run and chat with the model
lemonade run user.LFM-2.5-Coder-2.6B-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Schnuckade/LFM-2.5-Coder-2.6B with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Schnuckade/LFM-2.5-Coder-2.6B: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 Schnuckade/LFM-2.5-Coder-2.6B:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Schnuckade/LFM-2.5-Coder-2.6B with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Schnuckade/LFM-2.5-Coder-2.6B: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 "Schnuckade/LFM-2.5-Coder-2.6B: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"
| {{- bos_token -}} | |
| {%- set preserve_thinking = preserve_thinking | default(false) -%} | |
| {%- macro format_arg_value(arg_value) -%} | |
| {%- if arg_value is string -%} | |
| {{- "'" + (arg_value | replace("\\", "\\\\") | replace("'", "\\'") | replace("\n", "\\n") | replace("\r", "\\r")) + "'" -}} | |
| {%- elif arg_value is mapping or arg_value is iterable -%} | |
| {{- arg_value | tojson -}} | |
| {%- else -%} | |
| {{- arg_value | string -}} | |
| {%- endif -%} | |
| {%- endmacro -%} | |
| {%- macro parse_content(content) -%} | |
| {%- if content is string -%} | |
| {{- content -}} | |
| {%- elif content is mapping -%} | |
| {{- content | tojson -}} | |
| {%- elif content is iterable -%} | |
| {%- set _ns = namespace(result="") -%} | |
| {%- for item in content -%} | |
| {%- if item is string -%} | |
| {%- set _ns.result = _ns.result + item -%} | |
| {%- elif item is mapping and item.get("type") == "image" -%} | |
| {%- set _ns.result = _ns.result + "<image>" -%} | |
| {%- elif item is mapping and item.get("type") == "text" -%} | |
| {%- set _ns.result = _ns.result + ((item.get("text") or "") | string) -%} | |
| {%- else -%} | |
| {%- set _ns.result = _ns.result + (item | tojson) -%} | |
| {%- endif -%} | |
| {%- endfor -%} | |
| {{- _ns.result -}} | |
| {%- endif -%} | |
| {%- endmacro -%} | |
| {%- macro render_tool_calls(tool_calls) -%} | |
| {%- set tool_calls_ns = namespace(tool_calls=[]) -%} | |
| {%- for tool_call in tool_calls -%} | |
| {%- set func = tool_call["function"] if "function" in tool_call else tool_call -%} | |
| {%- set func_name = func["name"] -%} | |
| {%- set func_args = func.get("arguments") -%} | |
| {%- set args_ns = namespace(arg_strings=[]) -%} | |
| {%- if func_args is mapping -%} | |
| {%- for arg_name, arg_value in func_args.items() -%} | |
| {%- set args_ns.arg_strings = args_ns.arg_strings + [arg_name + "=" + format_arg_value(arg_value)] -%} | |
| {%- endfor -%} | |
| {%- elif func_args is string and (func_args | trim) not in ["", "{}", "null"] -%} | |
| {{- raise_exception("Tool call arguments must be a mapping, got a JSON-encoded string: parse arguments with json.loads() before applying the chat template") -}} | |
| {%- endif -%} | |
| {%- set tool_calls_ns.tool_calls = tool_calls_ns.tool_calls + [func_name + "(" + (args_ns.arg_strings | join(", ")) + ")"] -%} | |
| {%- endfor -%} | |
| {{- "<|tool_call_start|>[" + (tool_calls_ns.tool_calls | join(", ")) + "]<|tool_call_end|>" -}} | |
| {%- endmacro -%} | |
| {%- set ns = namespace(system_prompt="", last_user_index=-1) -%} | |
| {%- if messages and messages[0]["role"] == "system" -%} | |
| {%- if messages[0].get("content") -%} | |
| {%- set ns.system_prompt = parse_content(messages[0]["content"]) -%} | |
| {%- endif -%} | |
| {%- set messages = messages[1:] -%} | |
| {%- endif -%} | |
| {%- if tools -%} | |
| {%- set ns.system_prompt = ns.system_prompt + ("\n" if ns.system_prompt else "") + "List of tools: [" -%} | |
| {%- for tool in tools -%} | |
| {%- if tool is not string -%} | |
| {%- set tool = tool | tojson -%} | |
| {%- endif -%} | |
| {%- set ns.system_prompt = ns.system_prompt + tool -%} | |
| {%- if not loop.last -%} | |
| {%- set ns.system_prompt = ns.system_prompt + ", " -%} | |
| {%- endif -%} | |
| {%- endfor -%} | |
| {%- set ns.system_prompt = ns.system_prompt + "]" -%} | |
| {%- endif -%} | |
| {%- if ns.system_prompt -%} | |
| {{- "<|im_start|>system\n" + ns.system_prompt + "<|im_end|>\n" -}} | |
| {%- endif -%} | |
| {%- for message in messages -%} | |
| {%- if message["role"] == "user" -%} | |
| {%- set ns.last_user_index = loop.index0 -%} | |
| {%- endif -%} | |
| {%- endfor -%} | |
| {%- for message in messages -%} | |
| {{- "<|im_start|>" + message.role + "\n" -}} | |
| {%- if message.role == "assistant" -%} | |
| {%- generation -%} | |
| {%- set keep_thinking = preserve_thinking or loop.index0 > ns.last_user_index -%} | |
| {%- set thinking = message.thinking or message.reasoning or message.reasoning_content -%} | |
| {%- set thinking = thinking if thinking is string else "" -%} | |
| {%- if thinking and keep_thinking -%} | |
| {{- "<think>" + thinking + "</think>" -}} | |
| {%- endif -%} | |
| {%- set _cfm_tag = "CONTINUE_FINAL_MESSAGE_TAG " -%} | |
| {%- set _has_cfm = false -%} | |
| {%- set content = "" -%} | |
| {%- if message.get("content") -%} | |
| {%- set content = parse_content(message.content) -%} | |
| {%- endif -%} | |
| {%- if not keep_thinking and "</think>" in content -%} | |
| {%- set content = content.split("</think>")[-1] | trim -%} | |
| {%- endif -%} | |
| {%- if content.endswith(_cfm_tag) -%} | |
| {%- set _has_cfm = true -%} | |
| {%- set _trunc_len = (content | length) - (_cfm_tag | length) -%} | |
| {%- set content = content[:_trunc_len] -%} | |
| {%- endif -%} | |
| {{- content -}} | |
| {%- if message.tool_calls -%} | |
| {{- render_tool_calls(message.tool_calls) -}} | |
| {%- endif -%} | |
| {%- if _has_cfm -%} | |
| {{- _cfm_tag -}} | |
| {%- endif -%} | |
| {{- "<|im_end|>\n" -}} | |
| {%- endgeneration -%} | |
| {%- else %} | |
| {%- if message.get("content") -%} | |
| {{- parse_content(message["content"]) -}} | |
| {%- endif -%} | |
| {{- "<|im_end|>\n" -}} | |
| {%- endif %} | |
| {%- endfor -%} | |
| {%- if add_generation_prompt -%} | |
| {{- "<|im_start|>assistant\n<think>" -}} | |
| {%- endif -%} | |