---
title: README
emoji: 📈
colorFrom: purple
colorTo: pink
sdk: gradio
pinned: false
sdk_version: 5.46.0
---

# Halley AI on Hugging Face

High-quality, Apple-Silicon–optimized **MLX** builds, tools, and evals — focused on practical, on-prem inference for small teams.
> We publish **Mixture-of-Experts (MoE)** models and MLX quantizations tuned for M-series Macs (Metal + unified memory).  
> Target use: fast, reliable **interactive chat** and light batch workloads.

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## 🚀 Featured models

### gpt-oss-20b (MLX)

| Repo | Bits/GS | Footprint | Notes |
|---|---:|---:|---|
| [halley-ai/gpt-oss-20b-MLX-5bit-gs32](https://huggingface.co/halley-ai/gpt-oss-20b-MLX-5bit-gs32) | Q5 / 32 | ~15.8 GB | Small drop vs 6-bit (~3–6% PPL); “fits‑24GB” unified memory. |
| [halley-ai/gpt-oss-20b-MLX-6bit-gs32](https://huggingface.co/halley-ai/gpt-oss-20b-MLX-6bit-gs32) | Q6 / 32 | ~18.4 GB | Best of the group; strong quality/footprint tradeoff. |

### gpt-oss-120b (MLX)

| Repo | Bits/GS | Memory | Notes |
|---|---:|---|---|
| [halley-ai/gpt-oss-120b-MLX-8bit-gs32](https://huggingface.co/halley-ai/gpt-oss-120b-MLX-8bit-gs32) | Q8 / 32 | ~63.42 GB | Reference int8; stable and simple to use. |
| [halley-ai/gpt-oss-120b-MLX-bf16](https://huggingface.co/halley-ai/gpt-oss-120b-MLX-bf16) | bf16 | ~65.28 GB | Non-quantized reference for evaluation/ground truth. |

### Qwen3-Next-80B-A3B-Instruct (MLX)

| Repo | Bits/GS | Footprint | Notes |
|---|---:|---:|---|
| [halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-6bit-gs64](https://huggingface.co/halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-6bit-gs64) | Q6 / 64 | ~64.92 GB | Quality pick; matched bf16 on our PPL run (5.14). |
| [halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-5bit-gs32](https://huggingface.co/halley-ai/Qwen3-Next-80B-A3B-Instruct-MLX-5bit-gs32) | Q5 / 32 | ~59.86 GB | Balanced; near‑par PPL (5.20) and strong deterministic math. |

Perplexity reported with our fast preset on WikiText‑2 (raw, test). See repository docs for exact commands.

**Format:** MLX (not GGUF). For Linux/Windows or non-MLX stacks, use a GGUF build with llama.cpp.