Instructions to use KenWu/LeLM-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KenWu/LeLM-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="KenWu/LeLM-GGUF",
	filename="LeLM-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 KenWu/LeLM-GGUF 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 KenWu/LeLM-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf KenWu/LeLM-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf KenWu/LeLM-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf KenWu/LeLM-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 KenWu/LeLM-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf KenWu/LeLM-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 KenWu/LeLM-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf KenWu/LeLM-GGUF:Q4_K_M

Use Docker

docker model run hf.co/KenWu/LeLM-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use KenWu/LeLM-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/KenWu/LeLM-GGUF:Q4_K_M

Ollama
How to use KenWu/LeLM-GGUF with Ollama:
```
ollama run hf.co/KenWu/LeLM-GGUF:Q4_K_M
```

Unsloth Studio

How to use KenWu/LeLM-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 KenWu/LeLM-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 KenWu/LeLM-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for KenWu/LeLM-GGUF to start chatting

How to use KenWu/LeLM-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KenWu/LeLM-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": "KenWu/LeLM-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use KenWu/LeLM-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KenWu/LeLM-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 KenWu/LeLM-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use KenWu/LeLM-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KenWu/LeLM-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "KenWu/LeLM-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use KenWu/LeLM-GGUF with Docker Model Runner:
```
docker model run hf.co/KenWu/LeLM-GGUF:Q4_K_M
```

Lemonade

How to use KenWu/LeLM-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull KenWu/LeLM-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LeLM-GGUF-Q4_K_M

List all available models

lemonade list

LeLM-GGUF

GGUF quantization of KenWu/LeLM, an NBA take analysis model fine-tuned on Qwen3-8B.

Available Quantizations

File	Quant	Size	Description
`LeLM-Q4_K_M.gguf`	Q4_K_M	4.7 GB	Best balance of quality and size

Usage with Ollama

Create a Modelfile:

FROM ./LeLM-Q4_K_M.gguf

PARAMETER temperature 0.7
PARAMETER top_p 0.9

SYSTEM You are LeLM, an expert NBA analyst. Fact-check basketball takes using real statistics. Be direct, witty, and back everything with numbers.

Then run:

ollama create lelm -f Modelfile
ollama run lelm "Fact check: LeBron is washed"

Usage with llama.cpp

llama-cli -m LeLM-Q4_K_M.gguf -p "Fact check this NBA take: Steph Curry is the GOAT" -n 512

Model Details

Base model: Qwen3-8B
Fine-tuning: LoRA (r=64, alpha=128) with SFT on NBA take analysis data
Training: 3 epochs, 915 steps, final loss 0.288
LoRA adapter: KenWu/LeLM

Part of LeGM-Lab

This model powers LeGM-Lab, an LLM-powered NBA take analysis and roasting bot.

Downloads last month: 25

GGUF

Model size

8B params

Architecture

qwen3

Hardware compatibility

4-bit

Model tree for KenWu/LeLM-GGUF

Base model

Qwen/Qwen3-8B-Base

Finetuned

Qwen/Qwen3-8B

Quantized

(341)

this model