README.md

---
library_name: mlx
pipeline_tag: text-generation
inference: false
license: apache-2.0
base_model: Qwen/Qwen3-Next-80B-A3B-Instruct
base_model_relation: quantized
tags:
- apple-silicon
- metal
- arm64
- 4-bit
- group-size-64
- mlx
- mlx-lm
- qwen
- halley-ai
---

# Qwen3-Next-80B-A3B-Instruct — MLX 4-bit (group size 64)

**Summary.** This is a 4-bit (Q4) MLX quantization of Qwen3-Next-80B-A3B-Instruct with group size 64. Built for Apple Silicon with Metal acceleration.

- Base model: `Qwen/Qwen3-Next-80B-A3B-Instruct` (apache-2.0)
- Quantization: MLX Q4, `q_group_size=64` (some tensors may remain 16-bit for stability)
- Files: MLX weight shards + `config.json`; tokenizer files included for drop-in use
- Intended use: lightweight local inference on M-series Macs
- Not intended for: safety-critical decisions; outputs may be inaccurate or biased

## Requirements

Runs on Apple Silicon (M1 or newer) with macOS ≥ 13.5 via MLX (Metal).

- Not supported: Intel macOS / Linux / Windows (consider a GGUF build + llama.cpp instead).
- Memory guidance: large unified memory recommended (e.g., 64 GB+; 96 GB provides comfortable headroom). The effective GPU working set is capped by Metal’s budget; keep 5–10% headroom.

## How to use (MLX)

```bash
pip install mlx-lm
```

```python
from mlx_lm import load, generate

model, tokenizer = load("halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-4bit-gs64")
print(generate(
    model, tokenizer,
    prompt="Explain the Chudnovsky algorithm to compute π.",
    max_tokens=256, max_kv_size=512
))
```

```bash
python -m mlx_lm generate --model halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-4bit-gs64 \
  --prompt "Explain the Chudnovsky algorithm to compute pi." \
  --max-kv-size 512 --max-tokens 256
```

## Evaluation

Perplexity (PPL) streaming evaluation on WikiText-2 (raw, test); fast preset with `window=stride=4096`, ~100k tokens, EOS inserted between docs.

| Variant                 | PPL (ctx=4096, fast)                   |
|-------------------------|----------------------------------------|
| MLX bf16 (reference)    | 5.14                                   |
| MLX 6-bit (gs=64)       | 5.14 (≈0.0% vs bf16)                   |
| MLX 5-bit (gs=32)       | 5.20 (+1.2% vs bf16, +1.2% vs 6b/gs64) |
| MLX 4-bit (gs=64)       | 5.43 (+5.6% vs bf16, +5.6% vs 6b/gs64) |

### Interpretation

- 4-bit gs64 is the smallest footprint and shows a modest PPL increase versus 5/6‑bit.
- 5-bit gs32 is a strong “quality‑light” option if you can spare ~15 GB more.
- 6-bit gs64 matches bf16 on this corpus and is the quality pick.

Reproduce locally:

```bash
python python/scripts/test_perplexity-mlx.py \
  --model_path "/path/to/Qwen3-Next-80B-A3B-Instruct-4bit-gs64" \
  --fast --progress
```

## Conversion details (provenance)

```bash
python -m mlx_lm convert \
  --hf-path Qwen3-Next-80B-A3B-Instruct \
  --mlx-path /path/to/Qwen3-Next-80B-A3B-Instruct-4bit-gs64 \
  -q --q-bits 4 --q-group-size 64
```

- Some tensors (for example, embeddings/norms/router) may remain 16-bit for numerical stability.

## Sibling & reference models

- halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-6bit-gs64
- halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-5bit-gs32

## Verify quantization

```bash
jq '.quantization | {bits, group_size}' /path/to/export/config.json
```

## Limitations and biases

Compared to 5‑bit/6‑bit, Q4 may show small but noticeable quality drops on some tasks (for example, perplexity, instruction following). Choose this build for footprint/throughput over maximum accuracy.

## License and credits

- License: apache-2.0 (inherits from the base model)
- Base model: Qwen/Qwen3-Next-80B-A3B-Instruct
- Quantization: Halley AI Lab (MLX Q4, gs=64)
- Please cite both the base model and this repository when you use the weights.