Instructions to use Tigdora/lfm-2.5-coding-tool_gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tigdora/lfm-2.5-coding-tool_gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Tigdora/lfm-2.5-coding-tool_gguf",
	filename="LFM2.5-1.2B-Instruct.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

llama.cpp

How to use Tigdora/lfm-2.5-coding-tool_gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Tigdora/lfm-2.5-coding-tool_gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Tigdora/lfm-2.5-coding-tool_gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Tigdora/lfm-2.5-coding-tool_gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Tigdora/lfm-2.5-coding-tool_gguf: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 Tigdora/lfm-2.5-coding-tool_gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Tigdora/lfm-2.5-coding-tool_gguf: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 Tigdora/lfm-2.5-coding-tool_gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Tigdora/lfm-2.5-coding-tool_gguf:Q4_K_M

Use Docker

docker model run hf.co/Tigdora/lfm-2.5-coding-tool_gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use Tigdora/lfm-2.5-coding-tool_gguf with Ollama:
```
ollama run hf.co/Tigdora/lfm-2.5-coding-tool_gguf:Q4_K_M
```

Unsloth Studio new

How to use Tigdora/lfm-2.5-coding-tool_gguf 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 Tigdora/lfm-2.5-coding-tool_gguf 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 Tigdora/lfm-2.5-coding-tool_gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Tigdora/lfm-2.5-coding-tool_gguf to start chatting

Pi new

How to use Tigdora/lfm-2.5-coding-tool_gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Tigdora/lfm-2.5-coding-tool_gguf: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": "Tigdora/lfm-2.5-coding-tool_gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Tigdora/lfm-2.5-coding-tool_gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Tigdora/lfm-2.5-coding-tool_gguf: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 Tigdora/lfm-2.5-coding-tool_gguf:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Tigdora/lfm-2.5-coding-tool_gguf with Docker Model Runner:
```
docker model run hf.co/Tigdora/lfm-2.5-coding-tool_gguf:Q4_K_M
```

Lemonade

How to use Tigdora/lfm-2.5-coding-tool_gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Tigdora/lfm-2.5-coding-tool_gguf:Q4_K_M

Run and chat with the model

lemonade run user.lfm-2.5-coding-tool_gguf-Q4_K_M

List all available models

lemonade list

🧠 LFM-2.5-1.2B-Coding-Tools

This is a fine-tuned version of Liquid LFM-2.5-1.2B-Instruct, specialized for Python coding and native tool calling. It was trained using Unsloth on a hybrid dataset of coding instructions and Pythonic function calls.

📉 Training Results & Metrics

This model was fine-tuned on a Google Colab Tesla T4 instance. The following metrics were recorded during the final training run.

Metric	Value	Description
Final Loss	`0.7431`	The model's error rate at the final step.
Average Train Loss	`0.8274`	The average error rate across the entire session.
Epochs	`0.96`	Completed ~1 full pass over the dataset.
Global Steps	`60`	Total number of optimizer updates.
Runtime	`594s` (~10 min)	Total wall-clock time for training.
Samples/Second	`0.808`	Throughput speed on T4 GPU.
Gradient Norm	`0.345`	Indicates stable training (no exploding gradients).
Learning Rate	`3.64e-6`	Final learning rate after decay.
Total FLOS	`2.07e15`	Total floating-point operations computed.

🛠️ Hardware & Framework

Hardware: NVIDIA Tesla T4 (Google Colab Free Tier)
Framework: Unsloth (PyTorch)
Quantization: 4-bit (QLoRA)
Optimizer: AdamW 8-bit

View Raw Training Log (JSON)

{
  "_runtime": 348,
  "_step": 60,
  "_timestamp": 1770910365.0772636,
  "_wandb.runtime": 348,
  "total_flos": 2069937718053888,
  "train/epoch": 0.96,
  "train/global_step": 60,
  "train/grad_norm": 0.3452725112438202,
  "train/learning_rate": 0.000003636363636363636,
  "train/loss": 0.7431,
  "train_loss": 0.8273822158575058,
  "train_runtime": 594.2969,
  "train_samples_per_second": 0.808,
  "train_steps_per_second": 0.101
}

Downloads last month: 58

GGUF

Model size

1B params

Architecture

lfm2

Hardware compatibility

4-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for Tigdora/lfm-2.5-coding-tool_gguf

Base model

LiquidAI/LFM2.5-1.2B-Base

Finetuned

LiquidAI/LFM2.5-1.2B-Instruct

Quantized

(48)

this model

Tigdora
/

lfm-2.5-coding-tool_gguf

🧠 LFM-2.5-1.2B-Coding-Tools

📉 Training Results & Metrics

🛠️ Hardware & Framework

Model tree for Tigdora/lfm-2.5-coding-tool_gguf

Datasets used to train Tigdora/lfm-2.5-coding-tool_gguf