QwQ-32B-MLX-Q5

QwQ-32B-MLX-Q5

QwQ-32B-MLX-Q5 is an MLX Q5 checkpoint derived from Qwen/QwQ-32B, intended for local text generation on Apple Silicon.

Intended use

• Local text generation and chat-style prompting on Apple Silicon
• MLX-LM experimentation with the declared upstream model family
• Offline or operator-controlled inference workflows

Out of scope

• Safety-critical decisions without domain expert review
• Claims of benchmark superiority not backed by published evaluation data
• Non-MLX runtime guarantees; this card documents the shipped HF checkpoint, not every possible serving stack

Training and conversion metadata

This card only reports metadata present in the Hugging Face repository, existing card frontmatter, or public config files. Missing benchmark, dataset, or training-run details are left explicit rather than reconstructed.

Tested inference path

Inference for this checkpoint has been tested with LibraxisAI/mlx-batch-server.
This is the recommended tested path for operator-controlled local inference on Apple Silicon.

This does not claim compatibility with every possible serving stack. It documents the path that has been exercised for this published checkpoint.

Usage

CLI

pip install mlx-lm

mlx_lm.generate \
  --model LibraxisAI/QwQ-32B-MLX-Q5 \
  --prompt "Summarize the key signals in this document and list the next action items." \
  --max-tokens 400

Python

from mlx_lm import load, generate

model, tokenizer = load("LibraxisAI/QwQ-32B-MLX-Q5")

prompt = "Summarize the key signals in this document and list the next action items."
response = generate(model, tokenizer, prompt=prompt, max_tokens=400)
print(response)

Multi-turn with the chat template

This checkpoint follows the tokenizer/chat-template contract inherited from Qwen/QwQ-32B when the template is present in the repository:

from mlx_lm import load, generate

model, tokenizer = load("LibraxisAI/QwQ-32B-MLX-Q5")

messages = [
    {"role": "user", "content": "Summarize the key signals in this document and list the next action items."},
]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
response = generate(model, tokenizer, prompt=prompt, max_tokens=400)
print(response)

Example output

No public sample output is currently declared for this checkpoint.

Quantization notes

Limitations

• No public benchmarks for this checkpoint are declared in the model metadata.
• No public benchmark claims are made by this card unless listed in the frontmatter.
• Validate outputs on your own domain data before relying on this checkpoint.
• Memory use and speed depend heavily on the exact Apple Silicon generation, unified-memory size, and prompt length.

License

apache-2.0. Check the upstream/base model license as well when a base model is declared.

Citation

@misc{libraxisai-qwq-32b-mlx-q5,
  title = {QwQ-32B-MLX-Q5},
  author = {LibraxisAI},
  year = {2026},
  howpublished = {\url{https://huggingface.co/LibraxisAI/QwQ-32B-MLX-Q5}},
  note = {MLX checkpoint published by LibraxisAI}
}

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