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

preview code

raw

history blame

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

	<!-- Provide a quick summary of what the model is/does. -->

	This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

	## 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](https://huggingface.co/huihui-ai/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated) created with abliteration (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.

	### 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.