Text Generation
Transformers
Safetensors
MLX
English
code
code-review
programming
qwen2.5
bug-detection
Instructions to use xunker/CodeLens-7B-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xunker/CodeLens-7B-MLX with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="xunker/CodeLens-7B-MLX")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("xunker/CodeLens-7B-MLX", dtype="auto") - MLX
How to use xunker/CodeLens-7B-MLX 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("xunker/CodeLens-7B-MLX") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- vLLM
How to use xunker/CodeLens-7B-MLX with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "xunker/CodeLens-7B-MLX" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "xunker/CodeLens-7B-MLX", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/xunker/CodeLens-7B-MLX
- SGLang
How to use xunker/CodeLens-7B-MLX 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 "xunker/CodeLens-7B-MLX" \ --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": "xunker/CodeLens-7B-MLX", "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 "xunker/CodeLens-7B-MLX" \ --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": "xunker/CodeLens-7B-MLX", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - MLX LM
How to use xunker/CodeLens-7B-MLX with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "xunker/CodeLens-7B-MLX" --prompt "Once upon a time"
- Docker Model Runner
How to use xunker/CodeLens-7B-MLX with Docker Model Runner:
docker model run hf.co/xunker/CodeLens-7B-MLX