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"
File size: 5,443 Bytes
c861dcd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 | {{- 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 -%}
|