Instructions to use bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf with Transformers:

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

pipe = pipeline("text-generation", model="bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf", dtype="auto")

llama-cpp-python

How to use bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf",
	filename="Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-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 bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-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 bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf:Q4_K_M

Use Docker

docker model run hf.co/bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf:Q4_K_M

SGLang

How to use bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf 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 "bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf" \
    --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": "bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf",
		"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 "bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf" \
        --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": "bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf with Ollama:
```
ollama run hf.co/bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf:Q4_K_M
```

Unsloth Studio

How to use bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-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 bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-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 bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf to start chatting

How to use bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-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 bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf with Docker Model Runner:
```
docker model run hf.co/bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf:Q4_K_M
```

Lemonade

How to use bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf:Q4_K_M

Run and chat with the model

lemonade run user.Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf-Q4_K_M

List all available models

lemonade list

Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf / README.md

bkerler

Update README.md

c2c5e09 verified about 2 months ago

2.46 kB

license: apache-2.0
language:
  - en
base_model:
  - huihui-ai/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated
library_name: transformers
tags:
  - text-generation
  - reasoning
  - distillation
  - chain-of-thought
  - qwen
  - qwen3.6
  - mixture-of-experts
  - moe
  - lora
  - unsloth
  - abliterated
  - uncensored

Model Card for Model ID

This modelcard aims to be a base template for new models. It has been generated using this raw template.

Model Details

bkerler/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-gguf

This is an gguf version of huihui-ai/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated created with abliteration (see 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.

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.