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 / README.md

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	---
	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).