Instructions to use bestler/Code-Summary-Llama-3.2-3B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bestler/Code-Summary-Llama-3.2-3B-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="bestler/Code-Summary-Llama-3.2-3B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bestler/Code-Summary-Llama-3.2-3B-Instruct")
model = AutoModelForCausalLM.from_pretrained("bestler/Code-Summary-Llama-3.2-3B-Instruct", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

MLX

How to use bestler/Code-Summary-Llama-3.2-3B-Instruct 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-Llama-3.2-3B-Instruct")

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)

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use bestler/Code-Summary-Llama-3.2-3B-Instruct 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-Llama-3.2-3B-Instruct:F16
# Run inference directly in the terminal:
llama cli -hf bestler/Code-Summary-Llama-3.2-3B-Instruct:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf bestler/Code-Summary-Llama-3.2-3B-Instruct:F16
# Run inference directly in the terminal:
llama cli -hf bestler/Code-Summary-Llama-3.2-3B-Instruct:F16

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-Llama-3.2-3B-Instruct:F16
# Run inference directly in the terminal:
./llama-cli -hf bestler/Code-Summary-Llama-3.2-3B-Instruct:F16

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-Llama-3.2-3B-Instruct:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bestler/Code-Summary-Llama-3.2-3B-Instruct:F16

Use Docker

docker model run hf.co/bestler/Code-Summary-Llama-3.2-3B-Instruct:F16

LM Studio
Jan

vLLM

How to use bestler/Code-Summary-Llama-3.2-3B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bestler/Code-Summary-Llama-3.2-3B-Instruct"
# 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-Llama-3.2-3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bestler/Code-Summary-Llama-3.2-3B-Instruct:F16

SGLang

How to use bestler/Code-Summary-Llama-3.2-3B-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "bestler/Code-Summary-Llama-3.2-3B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bestler/Code-Summary-Llama-3.2-3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "bestler/Code-Summary-Llama-3.2-3B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bestler/Code-Summary-Llama-3.2-3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use bestler/Code-Summary-Llama-3.2-3B-Instruct with Ollama:
```
ollama run hf.co/bestler/Code-Summary-Llama-3.2-3B-Instruct:F16
```

Unsloth Studio

How to use bestler/Code-Summary-Llama-3.2-3B-Instruct 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-Llama-3.2-3B-Instruct 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-Llama-3.2-3B-Instruct 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-Llama-3.2-3B-Instruct to start chatting

How to use bestler/Code-Summary-Llama-3.2-3B-Instruct 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-Llama-3.2-3B-Instruct"

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-Llama-3.2-3B-Instruct"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use bestler/Code-Summary-Llama-3.2-3B-Instruct 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-Llama-3.2-3B-Instruct"

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-Llama-3.2-3B-Instruct" \
  --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-Llama-3.2-3B-Instruct 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-Llama-3.2-3B-Instruct"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "bestler/Code-Summary-Llama-3.2-3B-Instruct"
# 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-Llama-3.2-3B-Instruct",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use bestler/Code-Summary-Llama-3.2-3B-Instruct with Docker Model Runner:
```
docker model run hf.co/bestler/Code-Summary-Llama-3.2-3B-Instruct:F16
```

Lemonade

How to use bestler/Code-Summary-Llama-3.2-3B-Instruct with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bestler/Code-Summary-Llama-3.2-3B-Instruct:F16

Run and chat with the model

lemonade run user.Code-Summary-Llama-3.2-3B-Instruct-F16

List all available models

lemonade list

Hermes Agent

How to use bestler/Code-Summary-Llama-3.2-3B-Instruct 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-Llama-3.2-3B-Instruct"

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-Llama-3.2-3B-Instruct

Run Hermes

hermes

Atomic Chat

bestler/Summary-Agent-Llama-3.2-3B-Instruct

The Model bestler/Summary-Agent-Llama-3.2-3B-Instruct was converted to MLX format from mlx-community/Llama-3.2-3B-Instruct using mlx-lm version 0.20.6.

Code Summaries

This model is fine-tuned with LoRA on the CodeXGlue Dataset on performing Code Summaries for python source code.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("bestler/Summary-Agent-Llama-3.2-3B-Instruct")

prompt="hello"

if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )

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

Downloads last month: 48

Safetensors

Model size

3B params

Tensor type

F16

MLX

Hardware compatibility

Quantized

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Quantizations

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