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

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)

# 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")
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 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
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
```

starcoder2-15b-instruct-v0.1-4bit

File size: 2,263 Bytes

c063e8f

---
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)
```