Summary. This is a 5-bit (Q5) MLX quantization of Qwen3-Next-80B-A3B-Instruct with group size 32. Built for Apple Silicon with Metal acceleration.

Base model: Qwen/Qwen3-Next-80B-A3B-Instruct (apache-2.0)
Quantization: MLX Q5, q_group_size=32 (some tensors may remain 16-bit for stability)
Files: MLX weight shards + config.json; tokenizer files included for drop-in use
Intended use: local inference / research on M-series Macs
Not intended for: safety-critical decisions; outputs may be inaccurate or biased

Requirements

Built for Apple Silicon with Metal acceleration.

Memory: ≥96 GB recommended for comfortable headroom at large context lengths.

How to use (MLX)

pip install mlx-lm

python

from mlx_lm import load, generate

model, tokenizer = load("halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-5bit-gs32")
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-5bit-gs32 \
  --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)

Notes:

Numbers from local MLX runs on Apple Silicon; small variations are expected with tokenizer details, logits dtype, and token subset.

Interpretation

6-bit gs64 matches bf16 on this corpus; use it when maximum quality is the goal.
5-bit gs32 is a balanced pick: near-par PPL with a smaller footprint and strong deterministic math behavior.
4-bit gs64 trades a modest quality drop for the smallest size; good for constrained machines.

Reproduce locally:

bash

python python/scripts/test_perplexity-mlx.py \
  --model_path "/path/to/Qwen3-Next-80B-A3B-Instruct-5bit-gs32" \
  --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-5bit-gs32 \
  -q --q-bits 5 --q-group-size 32

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-4bit-gs64

Limitations and biases

Outputs may be factually wrong or unsafe. Do not use for medical, legal, or financial decisions without human review.

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 Q5, gs=32)
Please cite both the base model and this repository when you use the weights.