Instructions to use Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill",
	filename="LFM2-8B-A1B-Instruct-QwenDistill-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill 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 Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill: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 Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill: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 Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill:Q4_K_M

Use Docker

docker model run hf.co/Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill:Q4_K_M

LM Studio
Jan

vLLM

How to use Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill:Q4_K_M

Ollama
How to use Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill with Ollama:
```
ollama run hf.co/Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill:Q4_K_M
```

Unsloth Studio

How to use Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill 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 Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill 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 Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill to start chatting

How to use Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill: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": "Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill: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 Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill with Docker Model Runner:
```
docker model run hf.co/Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill:Q4_K_M
```

Lemonade

How to use Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill:Q4_K_M

Run and chat with the model

lemonade run user.LFM2-8B-A1B-Instruct-QwenDistill-Q4_K_M

List all available models

lemonade list

LFM2-8B-A1B-Instruct-QwenDistill - GGUF

This repository contains GGUF format quantizations of the model DavidAU/LFM2-8B-A1B-Instruct-Quantum-IQ1C-Qwen3.6-35B-A3B-DISTILL.

Quantization was performed locally using llama.cpp (build b9433).

Files Available

LFM2-8B-A1B-Instruct-QwenDistill-Q5_K_M.gguf (~5.64 GB) : Recommended balance. High accuracy, very low performance loss.
LFM2-8B-A1B-Instruct-QwenDistill-Q4_K_M.gguf (~4.81 GB) : Standard quantization. Fastest inference speed, smaller memory footprint.

Performance Test (Local CPU Inference)

The following benchmarks were obtained running the model locally on a standard laptop configuration, without GPU acceleration.

Hardware Specifications:

CPU: Intel Core i5 (11th Gen)
RAM: 32 GB
OS: Windows

Inference Results (`llama-cli.exe`):

LFM2-8B-A1B-Instruct-QwenDistill-Q4_K_M.gguf :
- Prompt Evaluation: ~41 t/s
- Token Generation: ~24 t/s
LFM2-8B-A1B-Instruct-QwenDistill-Q5_K_M.gguf :
- Prompt Evaluation: ~28 t/s
- Token Generation: ~20 t/s

Downloads last month: 69

GGUF

Model size

8B params

Architecture

lfm2moe

Hardware compatibility

4-bit

5-bit

Model tree for Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill

Base model

LiquidAI/LFM2-8B-A1B

Finetuned

DavidAU/LFM2-8B-A1B-Instruct-Quantum-IQ1C-Qwen3.6-35B-A3B-DISTILL

Quantized

(3)

this model

Collection including Tivaphraen/LFM2-8B-A1B-Instruct-QwenDistill