Instructions to use andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF with Ollama:
```
ollama run hf.co/andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF:Q4_K_M
```

Unsloth Studio

How to use andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF to start chatting

How to use andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF with Docker Model Runner:
```
docker model run hf.co/andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF:Q4_K_M
```

Lemonade

How to use andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Huihui-gemma-4-31B-it-abliterated-v2-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "andyjack/Huihui-gemma-4-31B-it-abliterated-v2-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

andyjack commited on May 13

Commit

9eaaa7f

verified ·

1 Parent(s): 65c06f3

Upload folder using huggingface_hub

Browse files

Files changed (6) hide show

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Huihui-gemma-4-31B-Q6_K.gguf +3 -0
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 ---
 license: apache-2.0
 ---

 ---
+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:
+- google/gemma-4-31B-it
+tags:
+- abliterated
+- uncensored
 ---
+# huihui-ai/Huihui-gemma-4-31B-it-abliterated-v2
+These are uncensored quantized versions of [google/gemma-4-31B-it](https://huggingface.co/google/gemma-4-31B-it) created with abliteration by Huihui-ai (see [remove-refusals-with-transformers](https://github.com/Sumandora/remove-refusals-with-transformers) to know more about it).
+This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.
+**Note** This is the new version, the first 5 layers have not been abliterated, with fewer warnings and rejections. Lower perplexity than those of the original model:
+I made GGUF's in Q8/Q6 for almost perfect full quality and Q4_K_M to fit on a single RTX 3090 or 4090.
+You can use llama.cpp and related utilities directly,
+```
+export GGML_CUDA_ENABLE_UNIFIED_MEMORY=1
+llama.cpp/build/bin/llama-server \
+  -m Huihui-gemma-4-31B-Q8_0.gguf \
+  --host 0.0.0.0 \
+  --port 11434 \
+  --api-key XXX \
+  --flash-attn on \
+  --cache-type-k q8_0 \
+  --cache-type-v q8_0 \
+  --n-gpu-layers 99 \
+  --split-mode layer \
+  --no-mmap \
+  --repeat-penalty 1.08 \
+  --repeat-last-n 256 \
+  -c 256000 \
+  -b 4096 \
+  -ub 1024 \
+  --parallel 1 \
+  --jinja
+```
+### 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 or 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. huihui.ai bears no responsibility for any consequences arising from its use.
+### Donation
+##### Your donation helps us continue our further development and improvement, a cup of coffee can do it.
+- bitcoin:
+```
+ bc1qejhwlrcc2pcz88uf7yacyeku3gtpcn2e3086ln
+```
+- Support our work on [Ko-fi](https://ko-fi.com/huihuiai)!

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