Instructions to use gggff123/lfmCodeEnhanced with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use gggff123/lfmCodeEnhanced 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 gggff123/lfmCodeEnhanced:Q4_K_M # Run inference directly in the terminal: llama cli -hf gggff123/lfmCodeEnhanced:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf gggff123/lfmCodeEnhanced:Q4_K_M # Run inference directly in the terminal: llama cli -hf gggff123/lfmCodeEnhanced:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf gggff123/lfmCodeEnhanced:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf gggff123/lfmCodeEnhanced:Q4_K_M
Use Docker
docker model run hf.co/gggff123/lfmCodeEnhanced:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use gggff123/lfmCodeEnhanced with Ollama:
ollama run hf.co/gggff123/lfmCodeEnhanced:Q4_K_M
- Unsloth Desktop
- Pi
How to use gggff123/lfmCodeEnhanced with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf gggff123/lfmCodeEnhanced:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "gggff123/lfmCodeEnhanced:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use gggff123/lfmCodeEnhanced with Docker Model Runner:
docker model run hf.co/gggff123/lfmCodeEnhanced:Q4_K_M
- Lemonade
How to use gggff123/lfmCodeEnhanced with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull gggff123/lfmCodeEnhanced:Q4_K_M
Run and chat with the model
lemonade run user.lfmCodeEnhanced-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use gggff123/lfmCodeEnhanced with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf gggff123/lfmCodeEnhanced: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 gggff123/lfmCodeEnhanced:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use gggff123/lfmCodeEnhanced with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf gggff123/lfmCodeEnhanced: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 "gggff123/lfmCodeEnhanced: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"
Update README.md
Browse files
README.md
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---
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license: mit
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---
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---
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license: mit
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language:
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- en
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base_model:
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- LiquidAI/LFM2.5-230M
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---
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## LFM 2.5 230m Code-Enhanced
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Lfm 2.5 230m code enhanced is a code-ish version of the previous lfm 2.5 230m (base) , this model however attempts to make the old model better at coding
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this uses the latest weight space programming paper along with deepseek v4.1 flash to manually change the model weights.
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1">
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<title>LFM CodeEnhanced AI</title>
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<style>
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* { box-sizing: border-box; }
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body {
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margin: 0;
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min-height: 100vh;
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display: grid;
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place-items: center;
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background: #080b14;
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font-family: Inter, Arial, sans-serif;
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}
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.logo {
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display: flex;
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align-items: center;
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gap: 18px;
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}
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svg { width: 100px; height: 100px; }
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.brand-name {
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font-size: 30px;
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font-weight: 850;
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letter-spacing: -1.5px;
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color: white;
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}
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.brand-name span {
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background: linear-gradient(100deg,#00e5ff,#9361ff);
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-webkit-background-clip: text;
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background-clip: text;
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color: transparent;
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}
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.tagline {
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margin-top: 7px;
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color: #8996b5;
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font-size: 10px;
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font-weight: 700;
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letter-spacing: 4px;
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}
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</style>
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</head>
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<body>
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<div class="logo">
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<svg viewBox="0 0 100 100" fill="none">
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<defs>
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<linearGradient id="g" x1="10" y1="10" x2="90" y2="90">
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<stop stop-color="#00e5ff"/>
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<stop offset="1" stop-color="#9361ff"/>
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</linearGradient>
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</defs>
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<path d="M50 4 91 27V73L50 96 9 73V27L50 4Z"
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fill="#111a30" stroke="url(#g)" stroke-width="2.5"/>
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<path d="m35 33-15 17 15 17M65 33l15 17-15 17"
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stroke="url(#g)" stroke-width="5"
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stroke-linecap="round" stroke-linejoin="round"/>
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<path d="m50 23 6 21 21 6-21 6-6 21-6-21-21-6 21-6 6-21Z"
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fill="url(#g)"/>
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<circle cx="50" cy="50" r="5" fill="white"/>
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</svg>
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<div>
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<div class="brand-name">LFM <span>CodeEnhanced</span></div>
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<div class="tagline">AI · CODE · CREATE</div>
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</div>
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</div>
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</body>
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</html>
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Currently has 3 quantizations
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--
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| Quantization | Quality |
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|----------------|--------|
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| BF16 | Highest quality <span style="background-color: #12CA12; color: black; padding: 2px 8px; border-radius: 10px; font-size: 12px; font-weight: bold;">Recommended</span>
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| Q4_KM | Mid Quality |
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| Q8 | Also good |
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---
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This model was generated by deepseek V4.1 FLASH by changing model weights.
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All credits to <b>liquid-ai, deepseek & weight space programming research paper</b>
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---
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