---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen3-Next-80B-A3B-Instruct
library_name: mlx-lm
pipeline_tag: text-generation
tags:
- mlx
- text-generation
- qwen
- mxfp4
- libraxisai
- MoE
- apple-silicon
- quantized
inference: false
widget:
- text: Summarize the operational risks in this deployment plan.
  example_title: Reasoning prompt
---

# Qwen3-Next-80B-A3B-Instruct-MLX-MXFP4

`Qwen3-Next-80B-A3B-Instruct-MLX-MXFP4` is an MLX MXFP4 checkpoint derived from `Qwen/Qwen3-Next-80B-A3B-Instruct`, 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

| Parameter | Value |
|---|---|
| Repository | `LibraxisAI/Qwen3-Next-80B-A3B-Instruct-MLX-MXFP4` |
| Base model | `Qwen/Qwen3-Next-80B-A3B-Instruct` |
| Task | `text-generation` |
| Library | `mlx-lm` |
| Format | MLX / Apple Silicon checkpoint |
| Quantization | MXFP4 |
| Architecture | Qwen3NextForCausalLM |
| Model files | 9 |
| Config model_type | `qwen3_next` |

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.

## Usage

### CLI

```bash
pip install mlx-lm

mlx_lm.generate \
  --model LibraxisAI/Qwen3-Next-80B-A3B-Instruct-MLX-MXFP4 \
  --prompt "Summarize the key signals in this document and list the next action items." \
  --max-tokens 400
```

### Python

```python
from mlx_lm import load, generate

model, tokenizer = load("LibraxisAI/Qwen3-Next-80B-A3B-Instruct-MLX-MXFP4")

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/Qwen3-Next-80B-A3B-Instruct` when the
template is present in the repository:

```python
from mlx_lm import load, generate

model, tokenizer = load("LibraxisAI/Qwen3-Next-80B-A3B-Instruct-MLX-MXFP4")

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. Run the usage example above against your own prompt or audio/image input to inspect behavior.

## Quantization notes

| Aspect | Original/base checkpoint | This checkpoint |
|---|---|---|
| Lineage | `Qwen/Qwen3-Next-80B-A3B-Instruct` | `LibraxisAI/Qwen3-Next-80B-A3B-Instruct-MLX-MXFP4` |
| Runtime target | Upstream runtime format | MLX on Apple Silicon |
| Quantization | Base precision or upstream-declared format | MXFP4 |
| Published quality delta | Not declared in public metadata | Not declared in public metadata |

## 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

```bibtex
@misc{libraxisai-qwen3-next-80b-a3b-instruct-mlx-mxfp4,
  title = {Qwen3-Next-80B-A3B-Instruct-MLX-MXFP4},
  author = {LibraxisAI},
  year = {2026},
  howpublished = {\url{https://huggingface.co/LibraxisAI/Qwen3-Next-80B-A3B-Instruct-MLX-MXFP4}},
  note = {MLX checkpoint published by LibraxisAI}
}
```

## Inference tested on

[`LibraxisAI/mlx-batch-server`](https://github.com/LibraxisAI/mlx-batch-server)

## Related

- Base model: [`Qwen/Qwen3-Next-80B-A3B-Instruct`](https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct)

---

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