Update README.md

README.md

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----
-license: apache-2.0
----
+---
+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
+- 5-bit
+- group-size-32
+- mlx
+- mlx-lm
+- qwen
+- halley-ai
+---
+
+# Qwen3-Next-80B-A3B-Instruct — MLX 5-bit (group size 32)
+
+**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)
+
+```bash
+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.