Add MLX quantized model

README.md

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+---
+base_model: openai/gpt-oss-120b
+library_name: mlx
+tags:
+  - rotorquant
+  - kv-cache-quantization
+  - gpt-oss
+  - openai
+  - moe
+  - quantized
+  - mlx
+  - 8bit
+license: apache-2.0
+pipeline_tag: text-generation
+---
+
+# GPT-OSS-120B - RotorQuant MLX 8-bit
+
+**8-bit weight-quantized MLX version** of [openai/gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) with RotorQuant KV-cache quantization. Optimized for Apple Silicon inference via the [MLX](https://github.com/ml-explore/mlx) framework. RotorQuant delivers 5.3x faster prefill and 28% faster decode compared to TurboQuant. GPT-OSS-120B is OpenAI's flagship open-weights Mixture-of-Experts model (Apache 2.0), approaching o4-mini quality for reasoning tasks.
+
+Approximate model size: **~120 GB**
+
+## Model Specifications
+
+| Property | Value |
+|---|---|
+| **Base Model** | [openai/gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) |
+| **Parameters** | 120 billion (MoE) |
+| **Architecture** | Mixture-of-Experts (MoE) Transformer |
+| **License** | Apache 2.0 (commercial use OK) |
+| **Weight Quantization** | 8-bit (~120 GB) |
+| **KV-Cache Quantization** | RotorQuant |
+| **Framework** | MLX (Apple Silicon) |
+
+## Quickstart
+
+```python
+from mlx_lm import load, generate
+from rotorquant import IsoQuantCache
+
+model, tokenizer = load("majentik/gpt-oss-120b-RotorQuant-MLX-8bit")
+
+prompt = "Explain the theory of relativity."
+response = generate(model, tokenizer, prompt=prompt, max_tokens=512)
+print(response)
+```
+
+## What is RotorQuant?
+
+[RotorQuant](https://github.com/scrya-com/rotorquant) applies block-diagonal rotations (Clifford algebra) for KV cache compression. Combined with 8-bit weight quantization in MLX, this provides a dual compression strategy with superior KV-cache performance.
+
+Key advantages over TurboQuant:
+- **5.3x faster prefill**
+- **28% faster decode**
+- Equivalent memory savings
+
+## KV-Cache Quantization Comparison
+
+| Method | Prefill Speed | Decode Speed | Memory Savings | Reference |
+|---|---|---|---|---|
+| **TurboQuant** | 1x (baseline) | 1x (baseline) | High | [arXiv: 2504.19874](https://arxiv.org/abs/2504.19874) |
+| **RotorQuant** | **5.3x faster** | **28% faster** | High | [GitHub](https://github.com/scrya-com/rotorquant) |
+
+## Memory Estimates (GPT-OSS-120B)
+
+| Precision | Approximate Size | MLX Variant |
+|---|---|---|
+| BF16 (original) | ~240 GB | -- |
+| **8-bit quantized** | **~120 GB** | **This model** |
+| 4-bit quantized | ~65 GB | [RotorQuant-MLX-4bit](https://huggingface.co/majentik/gpt-oss-120b-RotorQuant-MLX-4bit) |
+| 2-bit quantized | ~30 GB | [RotorQuant-MLX-2bit](https://huggingface.co/majentik/gpt-oss-120b-RotorQuant-MLX-2bit) |
+
+## Hardware Requirements
+
+This model requires approximately 120 GB of unified memory. Recommended hardware:
+- Apple M2 Ultra (192 GB)
+- Apple M3 Ultra (192 GB or 512 GB)
+- Mac Studio M4 Ultra (192 GB+)
+- Multi-device MLX inference for smaller Macs
+
+## See Also
+
+- [openai/gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) -- Base model
+- [majentik/gpt-oss-120b-RotorQuant](https://huggingface.co/majentik/gpt-oss-120b-RotorQuant) -- RotorQuant KV-cache only (transformers)
+- [majentik/gpt-oss-120b-RotorQuant-MLX-4bit](https://huggingface.co/majentik/gpt-oss-120b-RotorQuant-MLX-4bit) -- MLX 4-bit variant
+- [majentik/gpt-oss-120b-RotorQuant-MLX-2bit](https://huggingface.co/majentik/gpt-oss-120b-RotorQuant-MLX-2bit) -- MLX 2-bit variant
+- [majentik/gpt-oss-120b-TurboQuant-MLX-8bit](https://huggingface.co/majentik/gpt-oss-120b-TurboQuant-MLX-8bit) -- TurboQuant MLX 8-bit variant
+- [RotorQuant GitHub](https://github.com/scrya-com/rotorquant)
+- [MLX Framework](https://github.com/ml-explore/mlx)

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