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

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

lfm-tool / config.json

jerinaj

Trained with Unsloth - config

00ffb75 verified about 1 month ago

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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
	}