Text Generation
Transformers
Safetensors
MLX
starcoder2
code
conversational
Eval Results (legacy)
text-generation-inference
Instructions to use mlx-community/starcoder2-15b-instruct-v0.1-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlx-community/starcoder2-15b-instruct-v0.1-4bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mlx-community/starcoder2-15b-instruct-v0.1-4bit") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mlx-community/starcoder2-15b-instruct-v0.1-4bit") model = AutoModelForCausalLM.from_pretrained("mlx-community/starcoder2-15b-instruct-v0.1-4bit", 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - MLX
How to use mlx-community/starcoder2-15b-instruct-v0.1-4bit 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("mlx-community/starcoder2-15b-instruct-v0.1-4bit") 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
- LM Studio
- vLLM
How to use mlx-community/starcoder2-15b-instruct-v0.1-4bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mlx-community/starcoder2-15b-instruct-v0.1-4bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlx-community/starcoder2-15b-instruct-v0.1-4bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/mlx-community/starcoder2-15b-instruct-v0.1-4bit
- SGLang
How to use mlx-community/starcoder2-15b-instruct-v0.1-4bit 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/starcoder2-15b-instruct-v0.1-4bit" \ --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": "mlx-community/starcoder2-15b-instruct-v0.1-4bit", "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 "mlx-community/starcoder2-15b-instruct-v0.1-4bit" \ --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": "mlx-community/starcoder2-15b-instruct-v0.1-4bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - MLX LM
How to use mlx-community/starcoder2-15b-instruct-v0.1-4bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "mlx-community/starcoder2-15b-instruct-v0.1-4bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "mlx-community/starcoder2-15b-instruct-v0.1-4bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlx-community/starcoder2-15b-instruct-v0.1-4bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Docker Model Runner
How to use mlx-community/starcoder2-15b-instruct-v0.1-4bit with Docker Model Runner:
docker model run hf.co/mlx-community/starcoder2-15b-instruct-v0.1-4bit
- Atomic Chat
|
Download README.md from mlx-community/starcoder2-15b-instruct-v0.1-4bit: direct link, hf CLI and curl.
- Browser
- Download file 2.26 kB
-
https://huggingface.co/mlx-community/starcoder2-15b-instruct-v0.1-4bit/resolve/main/README.md
- Command line
-
hf download hf://mlx-community/starcoder2-15b-instruct-v0.1-4bit/README.md
-
curl -L -o README.md https://huggingface.co/mlx-community/starcoder2-15b-instruct-v0.1-4bit/resolve/main/README.md
2.26 kB
| license: bigcode-openrail-m | |
| library_name: transformers | |
| tags: | |
| - code | |
| - mlx | |
| base_model: bigcode/starcoder2-15b | |
| datasets: | |
| - bigcode/self-oss-instruct-sc2-exec-filter-50k | |
| pipeline_tag: text-generation | |
| model-index: | |
| - name: starcoder2-15b-instruct-v0.1 | |
| results: | |
| - task: | |
| type: text-generation | |
| dataset: | |
| name: LiveCodeBench (code generation) | |
| type: livecodebench-codegeneration | |
| metrics: | |
| - type: pass@1 | |
| value: 20.4 | |
| - task: | |
| type: text-generation | |
| dataset: | |
| name: LiveCodeBench (self repair) | |
| type: livecodebench-selfrepair | |
| metrics: | |
| - type: pass@1 | |
| value: 20.9 | |
| - task: | |
| type: text-generation | |
| dataset: | |
| name: LiveCodeBench (test output prediction) | |
| type: livecodebench-testoutputprediction | |
| metrics: | |
| - type: pass@1 | |
| value: 29.8 | |
| - task: | |
| type: text-generation | |
| dataset: | |
| name: LiveCodeBench (code execution) | |
| type: livecodebench-codeexecution | |
| metrics: | |
| - type: pass@1 | |
| value: 28.1 | |
| - task: | |
| type: text-generation | |
| dataset: | |
| name: HumanEval | |
| type: humaneval | |
| metrics: | |
| - type: pass@1 | |
| value: 72.6 | |
| - task: | |
| type: text-generation | |
| dataset: | |
| name: HumanEval+ | |
| type: humanevalplus | |
| metrics: | |
| - type: pass@1 | |
| value: 63.4 | |
| - task: | |
| type: text-generation | |
| dataset: | |
| name: MBPP | |
| type: mbpp | |
| metrics: | |
| - type: pass@1 | |
| value: 75.2 | |
| - task: | |
| type: text-generation | |
| dataset: | |
| name: MBPP+ | |
| type: mbppplus | |
| metrics: | |
| - type: pass@1 | |
| value: 61.2 | |
| - task: | |
| type: text-generation | |
| dataset: | |
| name: DS-1000 | |
| type: ds-1000 | |
| metrics: | |
| - type: pass@1 | |
| value: 40.6 | |
| # mlx-community/starcoder2-15b-instruct-v0.1-4bit | |
| This model was converted to MLX format from [`bigcode/starcoder2-15b-instruct-v0.1`]() using mlx-lm version **0.10.0**. | |
| Refer to the [original model card](https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1) for more details on the model. | |
| ## Use with mlx | |
| ```bash | |
| pip install mlx-lm | |
| ``` | |
| ```python | |
| from mlx_lm import load, generate | |
| model, tokenizer = load("mlx-community/starcoder2-15b-instruct-v0.1-4bit") | |
| response = generate(model, tokenizer, prompt="hello", verbose=True) | |
| ``` | |