license: other
base_model:
- huihui-ai/Huihui-Qwen3.5-9B-Claude-4.6-Opus-abliterated
tags: - exl3
- gptq
- quantized
- qwen3.5
Qwen3.5-9B-Abliterated-Claude-4.6-Opus-gptq-w4a16
Overview
Qwen3.5-9B-Abliterated-Claude-4.6-Opus-gptq-w4a16 is a GPTQ-quantized checkpoint intended for efficient GPU inference, published by groxaxo.
It is intended for open-source evaluation, reproducible experimentation, and compatible local or
hosted inference workflows. The wording below is deliberately limited to what can be verified
from this repository's metadata and artifacts.
The repository name identifies a behavior-modified or reduced-filtering lineage. That label describes the source or conversion history; it is not a guarantee of unrestricted behavior in every prompt or runtime. Test outputs carefully before sharing or deploying them.
At a glance
| Field | Details |
|---|---|
| Format | GPTQ |
| Source / base | huihui-ai/Huihui-Qwen3.5-9B-Claude-4.6-Opus-abliterated |
| Intended task | text-generation |
| License | other |
What is included
*.safetensors(2 files)config.jsongeneration_config.jsontokenizer.jsontokenizer_config.jsonchat_template.jinjaquantization_config.json- Additional configuration, tokenizer, processor, or shard files (9 visible artifacts total)
Quick start
vLLM (documented configuration)
vllm serve groxaxo/Qwen3.5-9B-Abliterated-Claude-4.6-Opus-gptq-w4a16 \
--quantization gptq_marlin \
--dtype float16 \
--trust-remote-code
This command is taken from the repository documentation. Adjust tensor parallelism, context
length, and cache settings to match your hardware and vLLM version.
Compatibility and responsible use
- Use a runtime that explicitly supports this format, architecture, and modality.
- Keep configuration, tokenizer, processor, projection, and weight files from the same revision together.
- Review the source model card and license before redistribution or deployment.
- Hardware needs depend on parameter count, context length, cache precision, quantization, and concurrency.
- Report reproducible issues with the runtime version, hardware, launch command, and a minimal example.
Quantization or conversion changes numerical behavior, memory use, and throughput relative to the source checkpoint; validate quality on your own workload.
Generated outputs may be inaccurate or unsuitable for a given use case. Users are responsible for
testing behavior, applying appropriate safeguards, and complying with applicable licenses and laws.
AutoRound quantization in GPTQ format (W4A16) of Qwen3.5-9B-Abliterated-Claude-4.6-Opus-Reasoning-Distilled. Calibrated with OpenCodeCal, group 64, sequence length 1024, norm 192, full attention in fp16.
Quantization Details
- Quantization method: AutoRound (GPTQ format)
- Bits/Config: W4A16 (group 64, seq 1024, norm 192, OpenCodeCal, full-attn fp16)
- Base model:
Qwen3.5-9B-Abliterated-Claude-4.6-Opus-Reasoning-Distilled