Summary. This is a 4-bit (Q4) MLX quantization of gpt-oss-20B (sparse Mixture-of-Experts, MPx4). Group size is 32.
Built for Apple Silicon with Metal acceleration.

Base model: openai/gpt-oss-20b (Apache-2.0)
Quantization: MLX Q4, q_group_size=32 (some tensors remain FP16 for stability)
Files: MLX weight shards + config.json; tokenizer files included for drop-in use
Footprint: ~13.11 GB on disk
Intended use: local inference / research 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 (use a GGUF build + llama.cpp instead).
RAM guidance: 24 GB+ required for Q4 gs=32 (does not work on Mac with only 16 GB).

How to use (MLX)

pip install mlx-lm transformers

python

# Python API (uses tokenizer bundled with this repo)
from mlx_lm import load, generate

model, tokenizer = load("halley-ai/gpt-oss-20b-MLX-4bit-gs32")
print(generate(
    model, tokenizer,
    prompt="Explain the Chudnovsky algorithm to compute π.",
    max_tokens=256, max_kv_size=512
))

bash

# CLI
python -m mlx_lm generate --model halley-ai/gpt-oss-20b-MLX-4bit-gs32 \
  --prompt "Explain the Chudnovsky algorithm to compute pi." \
  --max-kv-size 512 --max-tokens 256

Performance (Apple Silicon, real-world)

LM Studio / CLI (MLX, Q4 gs=32) ≈2k-token responses:

M1 Max (32 GB): ~62–72 tok/s, 0.30–0.40 s TTFB
M4 Pro (24 GB): ~80-90 tok/s, 0.40–0.60 TTFB
M3 Ultra (256 GB): ~130–140 tok/s, 0.20–0.30 s TTFB

Throughput varies with Mac model, context, and sampler settings.

Evaluation

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

Variant	PPL (ctx=4096)
MLX 8-bit (reference)	10.75
MLX 6-bit (gs=32)	10.46 (−2.7% vs 8-bit/gs64)
MLX 5-bit (gs=32)	11.11 (+3.3% vs 8-bit/gs64, +6.2% vs 6-bit/gs32)
MLX 4-bit (gs=32)	13.70 (+27.4% vs 8-bit/gs64, +31.0% vs 6-bit/gs32)

Interpretation

MLX 6-bit/gs32: Best of the group; edges out 8-bit/gs64 slightly at a smaller footprint.
MLX 5-bit/gs32: Small, consistent drop vs 6-bit/gs32 and 8-bit/gs64 (~3–6% PPL); strong “fits-16GB” option when GPU buffer limits matter.
MLX 8-bit/gs64: Solid reference; near‑FP16 quality at a larger footprint.
MLX 4-bit/gs32: Trades accuracy for footprint; use when RAM is constrained or throughput is the priority.

Conversion details (provenance)

bash

python -m mlx_lm convert \
  --hf-path openai/gpt-oss-20b \
  --mlx-path gpt-oss-20b-mlx-q4-gs32 \
  --q-bits 4 --q-group-size 32 -q

Some non-expert tensors (embeddings, norms, router) remain FP16.

Sibling & reference models

halley-ai/gpt-oss-20b-MLX-6bit-gs32
halley-ai/gpt-oss-20b-MLX-5bit-gs32
Reference (8-bit, upstream): lmstudio-community/gpt-oss-20b-MLX-8bit

Limitations & biases

Outputs may be factually wrong or unsafe. Don’t use for medical, legal, or financial decisions without human review. MoE models can be sensitive to prompt wording; prefer explicit instructions and structure.

License & credits

License: Apache-2.0 (inherits from base model)
Base model: OpenAI gpt-oss-20B
Quantization: Halley AI Lab (MLX Q4, gs=32)
Please cite both the base model and this repository when you use the weights.