Instructions to use BenjaminHelle/LFM2-350M-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use BenjaminHelle/LFM2-350M-code with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="BenjaminHelle/LFM2-350M-code", filename="LFM2-350M.Q4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use BenjaminHelle/LFM2-350M-code with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf BenjaminHelle/LFM2-350M-code:Q4_K_M # Run inference directly in the terminal: llama-cli -hf BenjaminHelle/LFM2-350M-code:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf BenjaminHelle/LFM2-350M-code:Q4_K_M # Run inference directly in the terminal: llama-cli -hf BenjaminHelle/LFM2-350M-code: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 BenjaminHelle/LFM2-350M-code:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf BenjaminHelle/LFM2-350M-code: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 BenjaminHelle/LFM2-350M-code:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf BenjaminHelle/LFM2-350M-code:Q4_K_M
Use Docker
docker model run hf.co/BenjaminHelle/LFM2-350M-code:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use BenjaminHelle/LFM2-350M-code with Ollama:
ollama run hf.co/BenjaminHelle/LFM2-350M-code:Q4_K_M
- Unsloth Studio
How to use BenjaminHelle/LFM2-350M-code 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 BenjaminHelle/LFM2-350M-code 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 BenjaminHelle/LFM2-350M-code to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for BenjaminHelle/LFM2-350M-code to start chatting
- Pi
How to use BenjaminHelle/LFM2-350M-code with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf BenjaminHelle/LFM2-350M-code: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": "BenjaminHelle/LFM2-350M-code:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use BenjaminHelle/LFM2-350M-code with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf BenjaminHelle/LFM2-350M-code: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 BenjaminHelle/LFM2-350M-code:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use BenjaminHelle/LFM2-350M-code with Docker Model Runner:
docker model run hf.co/BenjaminHelle/LFM2-350M-code:Q4_K_M
- Lemonade
How to use BenjaminHelle/LFM2-350M-code with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull BenjaminHelle/LFM2-350M-code:Q4_K_M
Run and chat with the model
lemonade run user.LFM2-350M-code-Q4_K_M
List all available models
lemonade list
| { | |
| "architectures": [ | |
| "Lfm2ForCausalLM" | |
| ], | |
| "block_auto_adjust_ff_dim": true, | |
| "block_dim": 1024, | |
| "block_ff_dim": 6656, | |
| "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": 1, | |
| "conv_L_cache": 3, | |
| "conv_bias": false, | |
| "conv_dim": 1024, | |
| "conv_dim_out": 1024, | |
| "conv_use_xavier_init": true, | |
| "torch_dtype": "bfloat16", | |
| "eos_token_id": 7, | |
| "hidden_size": 1024, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 6656, | |
| "layer_types": [ | |
| "conv", | |
| "conv", | |
| "full_attention", | |
| "conv", | |
| "conv", | |
| "full_attention", | |
| "conv", | |
| "conv", | |
| "full_attention", | |
| "conv", | |
| "full_attention", | |
| "conv", | |
| "full_attention", | |
| "conv", | |
| "full_attention", | |
| "conv" | |
| ], | |
| "max_position_embeddings": 128000, | |
| "model_name": "LiquidAI/LFM2-350M", | |
| "model_type": "lfm2", | |
| "norm_eps": 1e-05, | |
| "num_attention_heads": 16, | |
| "num_heads": 16, | |
| "num_hidden_layers": 16, | |
| "num_key_value_heads": 8, | |
| "pad_token_id": 0, | |
| "rope_parameters": { | |
| "rope_theta": 1000000.0, | |
| "rope_type": "default" | |
| }, | |
| "tie_word_embeddings": true, | |
| "unsloth_version": "2026.3.4", | |
| "use_cache": false, | |
| "use_pos_enc": true, | |
| "vocab_size": 65536 | |
| } |