---
language:
- en
license: other
base_model:
- huihui-ai/Huihui-Qwen3.5-9B-abliterated
tags:
- gptq
- 4bit
- quantized
- qwen
- text-generation
- gptq-pro
pipeline_tag: text-generation
library_name: transformers
---

# Huihui-Qwen3.5-9B-abliterated GPTQ-Pro 4bit (g64)

This is a GPTQ-Pro 4-bit quantization of `huihui-ai/Huihui-Qwen3.5-9B-abliterated`.

It was quantized with group size `64` and evaluated against the original model on Wikitext-2 using a strided perplexity setup, plus KL and token-agreement checks.

## Highlights

- Base model: `huihui-ai/Huihui-Qwen3.5-9B-abliterated`
- Quantization: GPTQ-Pro, 4-bit, group size `64`
- Calibration samples: `128`
- Quantization time: about `11.1` minutes
- Quantized strided perplexity: `9.6579`
- Original strided perplexity: `9.5234`
- Perplexity degradation: `1.41%`
- Average KL divergence vs original: `0.03423`
- Top-1 agreement vs original: `91.96%`
- Top-5 agreement vs original: `99.98%`

## Quality Notes

This quantized build stays very close to the source model in language modeling quality.

- Perplexity regression is small.
- KL divergence is low.
- Top-5 next-token agreement is effectively perfect.
- In practice, this should preserve most of the original model's behavior while reducing memory use substantially.

## Files

- `model-00001-of-00002.safetensors`
- `model-00002-of-00002.safetensors`
- `quantize_config.json`
- tokenizer and config files

## Load With Transformers / GPTQModel

```python
from gptqmodel import GPTQModel

model = GPTQModel.load(
    "groxaxo/Huihui-Qwen3.5-9B-abliterated-GPTQ-Pro-4bit-g64",
    device_map="auto",
    trust_remote_code=True,
)
```

## Evaluation Summary

Measured locally:

- Quantized strided PPL: `9.6579304371`
- Original strided PPL: `9.5233634665`
- Quantized chunked PPL: `11.6689118281`
- Original chunked PPL: `11.5080707440`
- KL divergence: `0.0342324856`
- Logit cosine similarity: `0.9935612157`

## Prompting

Use the same prompting and chat template behavior as the base model.

## Disclaimer

This repo contains only the quantized checkpoint. Please review the base model card for intended use, limitations, and licensing details.