Instructions to use tinyopsec/Qwen3.5-4B-heretic-v2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use tinyopsec/Qwen3.5-4B-heretic-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 tinyopsec/Qwen3.5-4B-heretic-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tinyopsec/Qwen3.5-4B-heretic-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 tinyopsec/Qwen3.5-4B-heretic-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tinyopsec/Qwen3.5-4B-heretic-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 tinyopsec/Qwen3.5-4B-heretic-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf tinyopsec/Qwen3.5-4B-heretic-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 tinyopsec/Qwen3.5-4B-heretic-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tinyopsec/Qwen3.5-4B-heretic-v2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/tinyopsec/Qwen3.5-4B-heretic-v2-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use tinyopsec/Qwen3.5-4B-heretic-v2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tinyopsec/Qwen3.5-4B-heretic-v2-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": "tinyopsec/Qwen3.5-4B-heretic-v2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tinyopsec/Qwen3.5-4B-heretic-v2-GGUF:Q4_K_M

Ollama
How to use tinyopsec/Qwen3.5-4B-heretic-v2-GGUF with Ollama:
```
ollama run hf.co/tinyopsec/Qwen3.5-4B-heretic-v2-GGUF:Q4_K_M
```
Unsloth Desktop

How to use tinyopsec/Qwen3.5-4B-heretic-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 tinyopsec/Qwen3.5-4B-heretic-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": "tinyopsec/Qwen3.5-4B-heretic-v2-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use tinyopsec/Qwen3.5-4B-heretic-v2-GGUF with Docker Model Runner:
```
docker model run hf.co/tinyopsec/Qwen3.5-4B-heretic-v2-GGUF:Q4_K_M
```

Lemonade

How to use tinyopsec/Qwen3.5-4B-heretic-v2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tinyopsec/Qwen3.5-4B-heretic-v2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-4B-heretic-v2-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use tinyopsec/Qwen3.5-4B-heretic-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 tinyopsec/Qwen3.5-4B-heretic-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 tinyopsec/Qwen3.5-4B-heretic-v2-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use tinyopsec/Qwen3.5-4B-heretic-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 tinyopsec/Qwen3.5-4B-heretic-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 "tinyopsec/Qwen3.5-4B-heretic-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"

Bible Assistant Qwen3.5-4B-v2 GGUF

GGUF quantized versions of Ttimms/Bible-Assistant-Qwen3.5-4B-v2.

Quantization Details

Quantization	Bits	Size	Use Case
Q8_0	8	~4.2 GB	High quality, minimal loss
Q6_K	6	~3.2 GB	Balanced quality/size
Q5_K_M	5	~2.7 GB	Good quality, reduced size
Q5_K_S	5	~2.5 GB	Faster inference, slight quality loss
Q4_K_M	4	~2.1 GB	Recommended default
Q4_K_S	4	~1.9 GB	Faster, smaller footprint
Q3_K_L	3	~1.6 GB	Mobile-friendly
Q3_K_M	3	~1.5 GB	Aggressive quantization
Q3_K_S	3	~1.4 GB	Mobile/edge devices
Q2_K	2	~1.1 GB	Extreme compression

VRAM Requirements

Quantization	VRAM	Device
Q8_0	~4.5 GB	Desktop GPU
Q6_K	~3.5 GB	Desktop GPU
Q5_K_M	~3.0 GB	Desktop GPU
Q4_K_M	~2.5 GB	Mid-range GPU / CPU
Q3_K_M	~2.0 GB	Laptop GPU
Q2_K	~1.5 GB	CPU-only

Usage

llama.cpp

./main -m model_q4_k_m.gguf -n 512 -p "What does the Bible say about..."

llama-cpp-python

from llama_cpp import Llama

llm = Llama(model_path="model_q4_k_m.gguf", n_gpu_layers=-1)
response = llm("What does the Bible say about...", max_tokens=512)
print(response["choices"][0]["text"])

LM Studio

Download any .gguf file from this repo
Open LM Studio → Load Model
Select and chat

Ollama

ollama pull hf.co/tinyopsec/bible-assistant-qwen35-4b-GGUF:q4_k_m
ollama run bible-assistant-qwen35-4b-GGUF:q4_k_m

Original Model

See Ttimms/Bible-Assistant-Qwen3.5-4B-v2 for model details, training data, and capabilities.

License

Licensed under Apache 2.0. See original model repo for details.

Downloads last month: -

GGUF

Model size

4B params

Architecture

qwen35

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for tinyopsec/Qwen3.5-4B-heretic-v2-GGUF

Base model

Qwen/Qwen3.5-4B-Base

Finetuned

Qwen/Qwen3.5-4B

Finetuned

Ttimms/Bible-Assistant-Qwen3.5-4B-v2

Quantized

(2)

this model