Instructions to use jerinaj/lfm-tool 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 jerinaj/lfm-tool 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 jerinaj/lfm-tool:Q4_K_M # Run inference directly in the terminal: llama cli -hf jerinaj/lfm-tool:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf jerinaj/lfm-tool:Q4_K_M # Run inference directly in the terminal: llama cli -hf jerinaj/lfm-tool: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 jerinaj/lfm-tool:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf jerinaj/lfm-tool: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 jerinaj/lfm-tool:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf jerinaj/lfm-tool:Q4_K_M
Use Docker
docker model run hf.co/jerinaj/lfm-tool:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use jerinaj/lfm-tool with Ollama:
ollama run hf.co/jerinaj/lfm-tool:Q4_K_M
- Unsloth Studio
How to use jerinaj/lfm-tool 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 jerinaj/lfm-tool 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 jerinaj/lfm-tool to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for jerinaj/lfm-tool to start chatting
- Docker Model Runner
How to use jerinaj/lfm-tool with Docker Model Runner:
docker model run hf.co/jerinaj/lfm-tool:Q4_K_M
- Lemonade
How to use jerinaj/lfm-tool with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull jerinaj/lfm-tool:Q4_K_M
Run and chat with the model
lemonade run user.lfm-tool-Q4_K_M
List all available models
lemonade list
- Atomic Chat
File size: 1,432 Bytes
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"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": "float16",
"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": 32768,
"model_name": "unsloth/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_theta": 1000000.0,
"unsloth_fixed": true,
"unsloth_version": "2026.7.4",
"use_cache": false,
"use_pos_enc": true,
"vocab_size": 65536
} |