Instructions to use navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S 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 navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S
# Run inference directly in the terminal:
llama cli -hf navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S
# Run inference directly in the terminal:
llama cli -hf navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S

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 navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S
# Run inference directly in the terminal:
./llama-cli -hf navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S

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 navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S

Use Docker

docker model run hf.co/navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S

LM Studio
Jan

vLLM

How to use navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S

Ollama
How to use navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S with Ollama:
```
ollama run hf.co/navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S
```

Unsloth Studio

How to use navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S 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 navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S 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 navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S to start chatting

How to use navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S

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": "navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S

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 "navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S" \
  --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"

Docker Model Runner
How to use navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S with Docker Model Runner:
```
docker model run hf.co/navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S
```

Lemonade

How to use navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S

Run and chat with the model

lemonade run user.Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S

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 navanchauhan/Huihui-Qwopus3.5-9B-v3-abliterated-TQ3_4S

Run Hermes

hermes

Atomic Chat

Qwopus3.5-9B-v3-abliterated-TQ3_4S

Pure TQ3_4S GGUF built locally from huihui-ai/Huihui-Qwopus3.5-9B-v3-abliterated.

Base source:

huihui-ai/Huihui-Qwopus3.5-9B-v3-abliterated
upstream family: Jackrong/Qwopus3.5-9B-v3

Artifacts:

pure GGUF size: 4,491,580,736 bytes (~4.17 GiB)
source F16 GGUF size: 17,920,693,568 bytes

Workflow used:

Download safetensors from Hugging Face.
Convert to F16 GGUF with convert_hf_to_gguf.py from turbo-tan/llama.cpp-tq3.
Quantize with llama-quantize --pure from the same repo.

Important implementation note:

The public llama.cpp-tq3 checkout needed local fixes so llama-quantize could actually quantize TQ3_4S end-to-end:
- expose TQ3_1S and TQ3_4S in tools/quantize/quantize.cpp
- map LLAMA_FTYPE_MOSTLY_TQ3_1S/TQ3_4S in src/llama-quant.cpp
- wire GGML_TYPE_TQ3_4S quantization in ggml/src/ggml.c

Exact quantization command used:

./build/bin/llama-quantize --pure \
  /path/to/Qwopus3.5-9B-v3-abliterated-f16.gguf \
  /path/to/Qwopus3.5-9B-v3-abliterated-TQ3_4S.gguf \
  TQ3_4S \
  16

Runtime:

target runtime: turbo-tan/llama.cpp-tq3

Example server command:

./build/bin/llama-server \
  -m /path/to/Qwopus3.5-9B-v3-abliterated-TQ3_4S.gguf \
  --host 127.0.0.1 --port 8080 \
  -ngl 99 -np 2 --kv-unified -c 32768 \
  -ctk q8_0 -ctv q8_0 -fa on \
  --jinja --reasoning on --reasoning-format deepseek --reasoning-budget 2048 \
  --alias qwopus-local

Local smoke test:

OpenAI-compatible server started successfully
/health returned {"status":"ok"}
/v1/models returned model id qwopus-local
completion prompt Write only the word ok. returned ok

Perplexity:

dataset: wikitext/wiki.test.raw
successful evaluation command:

./build/bin/llama-perplexity \
  -m /path/to/Qwopus3.5-9B-v3-abliterated-TQ3_4S.gguf \
  -f /path/to/wiki.test.raw \
  -ngl 0 -c 2048 -b 512 -ub 512 -fa off --no-kv-offload

final estimate: PPL = 10.7488 +/- 0.07717

Notes:

Lower perplexity is better.
The first high-throughput GPU eval attempt crashed late with a CUDA sync error; the above conservative eval settings completed successfully and are the reported numbers here.