Update README.md

README.md

@@ -17,13 +17,24 @@ tags:
 - halley-ai
 ---
 
-# halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-4bit-gs64
+# Qwen3-Next-80B-A3B-Instruct — MLX 4-bit (group size 64)
 
-This model [halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-4bit-gs64](https://huggingface.co/halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-4bit-gs64) was
-converted to MLX format from [Qwen/Qwen3-Next-80B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct)
-using mlx-lm version **0.28.0**.
+**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.
 
-## Use with mlx
+- 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
@@ -33,14 +44,73 @@ pip install mlx-lm
 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:
 
-prompt = "hello"
+```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.
 
-if tokenizer.chat_template is not None:
-    messages = [{"role": "user", "content": prompt}]
-    prompt = tokenizer.apply_chat_template(
-        messages, add_generation_prompt=True
-    )
+## Sibling & reference models
 
-response = generate(model, tokenizer, prompt=prompt, verbose=True)
+- 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.