Instructions to use mlx-community/bigcode-starcoder2-15b-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlx-community/bigcode-starcoder2-15b-4bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mlx-community/bigcode-starcoder2-15b-4bit")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mlx-community/bigcode-starcoder2-15b-4bit") model = AutoModelForCausalLM.from_pretrained("mlx-community/bigcode-starcoder2-15b-4bit") - MLX
How to use mlx-community/bigcode-starcoder2-15b-4bit with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("mlx-community/bigcode-starcoder2-15b-4bit") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- vLLM
How to use mlx-community/bigcode-starcoder2-15b-4bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mlx-community/bigcode-starcoder2-15b-4bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlx-community/bigcode-starcoder2-15b-4bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mlx-community/bigcode-starcoder2-15b-4bit
- SGLang
How to use mlx-community/bigcode-starcoder2-15b-4bit 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 "mlx-community/bigcode-starcoder2-15b-4bit" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlx-community/bigcode-starcoder2-15b-4bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "mlx-community/bigcode-starcoder2-15b-4bit" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlx-community/bigcode-starcoder2-15b-4bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - MLX LM
How to use mlx-community/bigcode-starcoder2-15b-4bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "mlx-community/bigcode-starcoder2-15b-4bit" --prompt "Once upon a time"
- Docker Model Runner
How to use mlx-community/bigcode-starcoder2-15b-4bit with Docker Model Runner:
docker model run hf.co/mlx-community/bigcode-starcoder2-15b-4bit
mlx-community/bigcode-starcoder2-15b-4bit
The Model mlx-community/bigcode-starcoder2-15b-4bit was converted to MLX format from bigcode/starcoder2-15b using mlx-lm version 0.21.1 by Focused.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/bigcode-starcoder2-15b-4bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
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Model tree for mlx-community/bigcode-starcoder2-15b-4bit
Base model
bigcode/starcoder2-15bDataset used to train mlx-community/bigcode-starcoder2-15b-4bit
Evaluation results
- pass@1 on CruxEval-Iself-reported48.100
- pass@1 on DS-1000self-reported33.800
- accuracy on GSM8K (PAL)self-reported65.100
- pass@1 on HumanEval+self-reported37.800
- pass@1 on HumanEvalself-reported46.300
- edit-smiliarity on RepoBench-v1.1self-reported74.080