Instructions to use bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-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 bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-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 bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-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 bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-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 bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-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": "bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF:Q4_K_M

Ollama
How to use bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF with Ollama:
```
ollama run hf.co/bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF:Q4_K_M
```

Unsloth Studio

How to use bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-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 bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-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 bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF to start chatting

How to use bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF"

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 bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF"

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 "bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

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

MLX LM

How to use bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF:Q4_K_M
```

Lemonade

How to use bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF-Q4_K_M

List all available models

lemonade list

bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF

This model was converted to GGUF format from bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF --hf-file code-summary-model-llama-3.1-8b-instruct-4bit-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF --hf-file code-summary-model-llama-3.1-8b-instruct-4bit-q4_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF --hf-file code-summary-model-llama-3.1-8b-instruct-4bit-q4_k_m.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF --hf-file code-summary-model-llama-3.1-8b-instruct-4bit-q4_k_m.gguf -c 2048

Downloads last month: 13

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

Model tree for bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit-Q4_K_M-GGUF

Base model

mlx-community/Meta-Llama-3.1-8B-Instruct-4bit

Quantized

bestler/Code-Summary-Model-Llama-3.1-8B-Instruct-4bit

Quantized

(1)

this model