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groxaxo/
gemma4-31b-abliterated-multimodal-awq8

Name: gemma4-31b-abliterated-multimodal-awq8
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Gemma4 31B Abliterated Multimodal AWQ8

Overview

gemma4-31b-abliterated-multimodal-awq8 is a weight-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	AWQ / AutoRound
Source / base	`shreyan35/gemma4-31b-abliterated-multimodal`
Intended task	`image-text-to-text`
License	the license declared in the repository files

What is included

*.safetensors (1 file)
config.json
generation_config.json
tokenizer.json
tokenizer_config.json
processor_config.json
chat_template.jinja
Additional configuration, tokenizer, processor, or shard files (8 visible artifacts total)

Quick start

vLLM (AWQ-compatible runtimes)

bash

vllm serve groxaxo/gemma4-31b-abliterated-multimodal-awq8 \
  --quantization awq_marlin \
  --dtype float16 \
  --trust-remote-code

The exact kernel and flags depend on the quantizer and architecture. Check the files and source model card before selecting a production serving configuration.

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.

This repository contains a compressed-tensors AWQ W8A16 checkpoint for shreyan35/gemma4-31b-abliterated-multimodal.

What is included

config.json with the compressed-tensors quantization config
model.safetensors
tokenizer.json and tokenizer_config.json
processor_config.json
chat_template.jinja
generation_config.json
recipe.yaml

Quantization summary

Format: compressed-tensors
Method: AWQ
Weight bits: 8
Activation bits: 16
Group size: 32
Weights: symmetric
Observer: mse
Duo scaling: enabled
Excluded from quantization: vision tower, multimodal projector/embed_vision, and lm_head

Recommended serving

Use vLLM for inference.

Tested runtime:

torch 2.10.0+cu128
transformers 5.5.1
compressed-tensors 0.14.0.1
vllm 0.19.0

bash

pip install -U "torch==2.10.0" "transformers==5.5.1" "compressed-tensors==0.14.0.1" "vllm==0.19.0"
vllm serve groxaxo/gemma4-31b-abliterated-multimodal-awq8 \
  --trust-remote-code \
  --dtype auto \
  --max-model-len 6144 \
  --served-model-name gemma4-31b-abliterated-multimodal-awq8

For local image inputs, add:

--allowed-local-media-path /path/to/images --limit-mm-per-prompt '{"image":1}'

For text-only long-context serving:

--limit-mm-per-prompt '{"image":0,"video":0,"audio":0}' --skip-mm-profiling --mm-processor-cache-gb 0 --max-model-len 10240

Transformers fallback

The checkpoint also loads with trust_remote_code=True through Transformers:

python

from transformers import AutoProcessor, AutoModelForImageTextToText

repo_id = "groxaxo/gemma4-31b-abliterated-multimodal-awq8"
processor = AutoProcessor.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained(
    repo_id,
    trust_remote_code=True,
    device_map="auto",
    torch_dtype="auto",
)

Notes

This checkpoint was built to preserve multimodal capability while avoiding quantization of the vision tower and projector modules.
If you only need a local OpenAI-compatible endpoint, point clients at http://127.0.0.1:1234/v1 after starting vLLM.