library_name: transformers
license: apache-2.0
license_link: https://ai.google.dev/gemma/docs/gemma_4_license
pipeline_tag: any-to-any
base_model:
- huihui-ai/Huihui-gemma-4-31B-it-abliterated-v2
tags: - gguf
- imatrix
- abliterated
- uncensored
- any-to-any
- image-text-to-text
groxaxo/Huihui-gemma-4-31B-it-abliterated-v2-GGUF
Overview
Huihui-gemma-4-31B-it-abliterated-v2-GGUF is a GGUF release for llama.cpp-compatible runtimes and local 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 | GGUF |
| Source / base | huihui-ai/Huihui-gemma-4-31B-it-abliterated-v2 |
| Intended task | any-to-any |
| License | apache-2.0 |
What is included
*.gguf(9 files)- Additional configuration, tokenizer, processor, or shard files (9 visible artifacts total)
Quick start
llama.cpp
Download a .gguf file that fits your available memory, then run it with a current llama.cpp
build:
llama-cli \
-m /path/to/model.gguf \
-p "Write a concise technical summary."
For vision or any-to-any models, download the matching multimodal projection file when one is
provided and follow the source model's modality-specific instructions.
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.
GGUF quantizations of huihui-ai/Huihui-gemma-4-31B-it-abliterated-v2, built with llama.cpp from the included imatrix_unsloth.gguf and the tensor distributions in the downloaded recipe bundle.
Included files:
mmproj-BF16.ggufimatrix_unsloth.ggufHuihui-gemma-4-31B-it-abliterated-v2-UD-IQ2_M.ggufHuihui-gemma-4-31B-it-abliterated-v2-UD-IQ2_XXS.ggufHuihui-gemma-4-31B-it-abliterated-v2-UD-IQ3_XXS.ggufHuihui-gemma-4-31B-it-abliterated-v2-UD-Q2_K_XL.ggufHuihui-gemma-4-31B-it-abliterated-v2-UD-Q3_K_XL.ggufHuihui-gemma-4-31B-it-abliterated-v2-UD-Q4_K_XL.ggufHuihui-gemma-4-31B-it-abliterated-v2-UD-Q5_K_XL.ggufHuihui-gemma-4-31B-it-abliterated-v2-UD-Q6_K_XL.ggufHuihui-gemma-4-31B-it-abliterated-v2-UD-Q8_K_XL.gguf
Usage warnings
- Risk of Sensitive or Controversial Outputs: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs.
- Not Suitable for All Audiences: Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security.
- Legal and Ethical Responsibilities: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal and ethical risks, and users are solely responsible for any consequences.
- Research and Experimental Use: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications.
- Monitoring and Review Recommendations: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content.
- No Default Safety Guarantees: Unlike standard models, this model has not undergone rigorous safety optimization.