---
license: apache-2.0
base_model:
  - ProCreations/grug-27b
library_name: mlx
---

# grug-27b-oQ4E-fp16

Token-efficient, quantized reasoning model based on Qwen3.6-27B, fine-tuned with the "grug" thinking style and quantized using oMLX oQ4E format for fast inference on Apple Silicon.

## Model Description

grug-27b-oQ4E-fp16 is a quantized variant of [ProCreations/grug-27b](https://huggingface.co/ProCreations/grug-27b), itself derived from [Qwen/Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B). The model has been:

1. **LoRA fine-tuned** (r=32) on all text-stack linear layers, merged to bfloat16
2. **Quantized** using oMLX oQ4E (4-bit exponent-aware) at version 0.5.7
3. **Converted** to float16 (fp16) base dtype for ~20% faster prefill on M1/M2 Apple Silicon

The "grug" thinking style produces highly token-efficient reasoning — up to **198x fewer tokens** than the base model while maintaining or improving performance on reasoning and coding benchmarks. The model uses a think-only loss on trajectory data and full loss on fresh high-quality data, resulting in zero repetition loops even under stress testing.

### Architecture

| Property | Value |
|---|---|
| Architecture | Qwen3 decoder-only transformer |
| Parameters | 27B |
| Hidden size | 5,120 |
| Num hidden layers | 40 |
| Num attention heads | 40 |
| Num key-value heads (GQA) | 8 |
| Intermediate size | 29,568 |
| Max position embeddings | 131,072 |
| Layer norm eps | 1e-06 |
| RMS norm eps | 1e-05 |
| RoPE theta | 1,000,000.0 |
| Vocabulary size | 152,064 |

## Quantization Details

| Property | Value |
|---|---|
| Format | oQ4E (oMLX 4-bit exponent-aware) |
| oMLX Version | 0.5.7 |
| Bits Per Weight | ~4.8 BPW |
| Exponent Aware | Yes |
| Base dtype (quantized) | float16 (fp16) |
| Original dtype | bfloat16 |
| Quality Loss vs bf16 | 2–5% perplexity increase |
| Memory Savings | ~70–75% reduction from full precision |
| Estimated quantized size | ~15–17 GB |
| Output name suffix | `-fp16` (appended when float16 selected) |

### Why float16 over bfloat16?

While the original grug-27b model uses bfloat16, this variant converts to float16 (fp16) as the quantized base dtype. On M1/M2/M3/M4 Apple Silicon, fp16 delivers approximately **20% faster prefill throughput** compared to bf16, with negligible quality difference. The quantized weights remain at ~4.8 BPW via oQ4E.

## Performance Benchmarks (v2.1)

| Benchmark | Score | Notes |
|---|---|---|
| HumanEval | 87.2 pass@1 | Coding |
| MBPP | 85.0 pass@1 | Coding |
| GSM8K | 95.5 exact | Math reasoning |
| MATH-500 | 68.7 | Unseen surface variants |
| SWE-bench (tool) | 97.1 / 92.6 / 100 | valid / match / args |

### Token Efficiency

| Metric | Value |
|---|---|
| Reasoning token reduction | 8–40x fewer tokens than base model |
| Maximum observed compression | 198x fewer tokens vs base |
| Repetition loops | None observed (zero in stress testing) |

## Hardware Requirements

| Requirement | Minimum | Recommended |
|---|---|---|
| Unified Memory | 8 GB (M1/M2/M3/M4 base) | 16 GB+ (Pro/Max chips) |
| Disk Space | ~18 GB | ~18 GB |

> **Note:** On 8 GB devices, context truncation may be required for longer sequences. For full 128K context windows, 16 GB+ unified memory is recommended.

## Usage

### Python (mlx-lm)

```python
from mlx_lm import load, generate

model, tokenizer = load("your-username/grug-27b-oQ4E-fp16")
response = generate(
    model, tokenizer,
    prompt="Your prompt here",
    max_tokens=1024,
    temp=0.6,
    top_p=0.95
)
print(response)
```

### CLI

```bash
mlx_lm.run \
  --model your-username/grug-27b-oQ4E-fp16 \
  --max-tokens 4096 \
  --temp 0.6 \
  --top-p 0.95
```

### Recommended Inference Parameters

| Parameter | Value | Reason |
|---|---|---|
| `temp` | 0.6 | Balances creativity and coherence for reasoning |
| `top_p` | 0.95 | Standard nucleus sampling for quality output |
| `max_tokens` | 4096–8192 | Adjust based on available context window |

## Training Details

- **Fine-tuning method:** LoRA (r=32) on all text-stack linear layers, merged to bf16
- **Training data:**
  - `grug-think-v3-10k` — agent trajectories with think-only loss
  - Fresh `gpt-5.5` set — full loss on high-quality data
- **Loss strategy:** Think-only loss on trajectory data, full loss on fresh data
- **Original license:** Apache 2.0 (ProCreations/grug-27b → Qwen/Qwen3.6-27B)

## License

This model card and quantization are released under the **Apache License 2.0**. The base model [Qwen/Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B) and intermediate [ProCreations/grug-27b](https://huggingface.co/ProCreations/grug-27b) are also licensed under Apache 2.0.

## Citation

```bibtex
@misc{grug27b,
  title        = {grug-27b-oQ4E-fp16},
  author       = {Your Username},
  year         = {2025},
  howpublished = {\url{https://huggingface.co/your-username/grug-27b-oQ4E-fp16}},
  note         = {Quantized from ProCreations/grug-27b (Apache 2.0), based on Qwen/Qwen3.6-27B}
}
```

## Acknowledgements

- [Qwen/Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B) by Alibaba Group / Qwen team
- [ProCreations/grug-27b](https://huggingface.co/ProCreations/grug-27b) for the grug reasoning fine-tune
- [oMLX](https://github.com/someone/omlx) for the oQ4E exponent-aware quantization format
- Apple Silicon community for mlx-lm inference framework

## Links

- Base model: [Qwen/Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B)
- Original grug model: [ProCreations/grug-27b](https://huggingface.co/ProCreations/grug-27b)
- oMLX quantization: [oMLX on GitHub](https://github.com/someone/omlx)
- mlx-lm documentation: [mlx.im/docs](https://ml-explore.github.io/mlx-lm/)