Instructions to use xunker/CodeLens-7B-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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", device_map="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
```
Atomic Chat

CodeLens-7B-MLX

File size: 2,083 Bytes

---
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
  - code
  - code-review
  - programming
  - qwen2.5
  - bug-detection
  - mlx
datasets:
  - sahil2801/CodeAlpaca-20k
language:
  - en
pipeline_tag: text-generation
library_name: transformers
model-index:
  - name: CodeLens-7B
    results: []
---

# [CodeLens-7B-MLX](https://huggingface.co/xunker/CodeLens-7B-MLX)

MLX version of [sriksven/CodeLens-7B](https://huggingface.co/sriksven/CodeLens-7B) in various oQ levels and dtypes.

Directory                                     | oQ Level | dtype    | size
----------------------------------------------|----------|----------|------
[CodeLens-7B-oQ4-bf16](CodeLens-7B-oQ4-bf16/) | 4-bit    | bfloat16 | 4.2GB
[CodeLens-7B-oQ4-fp16](CodeLens-7B-oQ4-fp16/) | 4-bit    | fp16     | 4.2GB
[CodeLens-7B-oQ6-bf16](CodeLens-7B-oQ6-bf16/) | 6-bit    | bfloat16 | 5.9GB
[CodeLens-7B-oQ6-fp16](CodeLens-7B-oQ6-fp16/) | 6-bit    | fp16     | 5.9GB
[CodeLens-7B-oQ8-bf16](CodeLens-7B-oQ8-bf16/) | 8-bit    | bfloat16 | 7.5GB
[CodeLens-7B-oQ8-fp16](CodeLens-7B-oQ8-fp16/) | 8-bit    | fp16     | 7.5GB

## Why choose FP16 over BFLOAT16/BF16?

On older Apple Silicon (M1 and M2), fp16 can be faster. Here are the details from [Muhammad Raza](https://muhammadraza.me/2026/gguf-vs-mlx-decision-guide/#two-traps-that-will-flip-your-results):

> A lot of MLX builds ship as bf16, and **on the M1 and M2 that data type does not get the accelerated path that fp16 does**. During prefill those weights run un-accelerated and the penalty multiplies across every input token, which is part of why some “MLX is slow” reports come from older hardware. [...]
>
> If you are on an M1 or M2 and MLX feels sluggish, check this before you blame the format.

## Hardware and Software

These were converted to MLX using [oMLX](https://github.com/jundot/omlx) [0.4.4](https://github.com/jundot/omlx/releases/tag/v0.4.4) on a 32GB Macbook Pro 2021 (M1 Pro). I cleared all my RAM so you don't have to.

## License

Apache 2.0, as per [original model](https://huggingface.co/sriksven/CodeLens-7B).