Instructions to use bunnycore/LMF-2.5-2B-Code-GGUF 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 bunnycore/LMF-2.5-2B-Code-GGUF 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 bunnycore/LMF-2.5-2B-Code-GGUF:Q5_K_M # Run inference directly in the terminal: llama cli -hf bunnycore/LMF-2.5-2B-Code-GGUF:Q5_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf bunnycore/LMF-2.5-2B-Code-GGUF:Q5_K_M # Run inference directly in the terminal: llama cli -hf bunnycore/LMF-2.5-2B-Code-GGUF:Q5_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 bunnycore/LMF-2.5-2B-Code-GGUF:Q5_K_M # Run inference directly in the terminal: ./llama-cli -hf bunnycore/LMF-2.5-2B-Code-GGUF:Q5_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 bunnycore/LMF-2.5-2B-Code-GGUF:Q5_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf bunnycore/LMF-2.5-2B-Code-GGUF:Q5_K_M
Use Docker
docker model run hf.co/bunnycore/LMF-2.5-2B-Code-GGUF:Q5_K_M
- LM Studio
- Jan
- Ollama
How to use bunnycore/LMF-2.5-2B-Code-GGUF with Ollama:
ollama run hf.co/bunnycore/LMF-2.5-2B-Code-GGUF:Q5_K_M
- Unsloth Desktop
- Pi
How to use bunnycore/LMF-2.5-2B-Code-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf bunnycore/LMF-2.5-2B-Code-GGUF:Q5_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": "bunnycore/LMF-2.5-2B-Code-GGUF:Q5_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use bunnycore/LMF-2.5-2B-Code-GGUF with Docker Model Runner:
docker model run hf.co/bunnycore/LMF-2.5-2B-Code-GGUF:Q5_K_M
- Lemonade
How to use bunnycore/LMF-2.5-2B-Code-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull bunnycore/LMF-2.5-2B-Code-GGUF:Q5_K_M
Run and chat with the model
lemonade run user.LMF-2.5-2B-Code-GGUF-Q5_K_M
List all available models
lemonade list
- Hermes Agent
How to use bunnycore/LMF-2.5-2B-Code-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf bunnycore/LMF-2.5-2B-Code-GGUF:Q5_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 bunnycore/LMF-2.5-2B-Code-GGUF:Q5_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use bunnycore/LMF-2.5-2B-Code-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf bunnycore/LMF-2.5-2B-Code-GGUF:Q5_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 "bunnycore/LMF-2.5-2B-Code-GGUF:Q5_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: 1,643 Bytes
9cea9fe | 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 | {
"architectures": [
"Lfm2ForCausalLM"
],
"block_auto_adjust_ff_dim": false,
"block_dim": 2048,
"block_ffn_dim_multiplier": 1.0,
"block_mlp_init_scale": 1.0,
"block_multiple_of": 256,
"block_norm_eps": 1e-05,
"block_out_init_scale": 1.0,
"block_use_swiglu": true,
"block_use_xavier_init": true,
"bos_token_id": 124894,
"conv_L_cache": 3,
"conv_bias": false,
"conv_dim": 2048,
"conv_use_xavier_init": true,
"torch_dtype": "bfloat16",
"eos_token_id": 124900,
"hidden_size": 2048,
"initializer_range": 0.02,
"intermediate_size": 10752,
"layer_types": [
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"full_attention",
"conv",
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"full_attention",
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"conv",
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],
"max_position_embeddings": 131072,
"model_type": "lfm2",
"norm_eps": 1e-05,
"num_attention_heads": 32,
"num_heads": 32,
"num_hidden_layers": 30,
"num_key_value_heads": 8,
"pad_token_id": 124893,
"rope_parameters": {
"rope_theta": 10000000.0,
"rope_type": "default"
},
"tie_word_embeddings": true,
"use_cache": true,
"use_pos_enc": true,
"vocab_size": 128000
} |