Instructions to use groxaxo/Huihui-Qwen3.5-9B-abliterated-GPTQ-Pro-4bit-g64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use groxaxo/Huihui-Qwen3.5-9B-abliterated-GPTQ-Pro-4bit-g64 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="groxaxo/Huihui-Qwen3.5-9B-abliterated-GPTQ-Pro-4bit-g64")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("groxaxo/Huihui-Qwen3.5-9B-abliterated-GPTQ-Pro-4bit-g64")
model = AutoModelForCausalLM.from_pretrained("groxaxo/Huihui-Qwen3.5-9B-abliterated-GPTQ-Pro-4bit-g64", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use groxaxo/Huihui-Qwen3.5-9B-abliterated-GPTQ-Pro-4bit-g64 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "groxaxo/Huihui-Qwen3.5-9B-abliterated-GPTQ-Pro-4bit-g64"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "groxaxo/Huihui-Qwen3.5-9B-abliterated-GPTQ-Pro-4bit-g64",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/groxaxo/Huihui-Qwen3.5-9B-abliterated-GPTQ-Pro-4bit-g64

SGLang

How to use groxaxo/Huihui-Qwen3.5-9B-abliterated-GPTQ-Pro-4bit-g64 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "groxaxo/Huihui-Qwen3.5-9B-abliterated-GPTQ-Pro-4bit-g64" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "groxaxo/Huihui-Qwen3.5-9B-abliterated-GPTQ-Pro-4bit-g64",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "groxaxo/Huihui-Qwen3.5-9B-abliterated-GPTQ-Pro-4bit-g64" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "groxaxo/Huihui-Qwen3.5-9B-abliterated-GPTQ-Pro-4bit-g64",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use groxaxo/Huihui-Qwen3.5-9B-abliterated-GPTQ-Pro-4bit-g64 with Docker Model Runner:
```
docker model run hf.co/groxaxo/Huihui-Qwen3.5-9B-abliterated-GPTQ-Pro-4bit-g64
```

Huihui-Qwen3.5-9B-abliterated-GPTQ-Pro-4bit-g64 / README.md

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	---
	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.