Instructions to use alexdenton/Qwen3.5-27B-heretic-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 alexdenton/Qwen3.5-27B-heretic-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 alexdenton/Qwen3.5-27B-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf alexdenton/Qwen3.5-27B-heretic-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 alexdenton/Qwen3.5-27B-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf alexdenton/Qwen3.5-27B-heretic-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 alexdenton/Qwen3.5-27B-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf alexdenton/Qwen3.5-27B-heretic-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 alexdenton/Qwen3.5-27B-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf alexdenton/Qwen3.5-27B-heretic-GGUF:Q4_K_M

Use Docker

docker model run hf.co/alexdenton/Qwen3.5-27B-heretic-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use alexdenton/Qwen3.5-27B-heretic-GGUF with Ollama:
```
ollama run hf.co/alexdenton/Qwen3.5-27B-heretic-GGUF:Q4_K_M
```

Unsloth Studio

How to use alexdenton/Qwen3.5-27B-heretic-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 alexdenton/Qwen3.5-27B-heretic-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 alexdenton/Qwen3.5-27B-heretic-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for alexdenton/Qwen3.5-27B-heretic-GGUF to start chatting

How to use alexdenton/Qwen3.5-27B-heretic-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf alexdenton/Qwen3.5-27B-heretic-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": "alexdenton/Qwen3.5-27B-heretic-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use alexdenton/Qwen3.5-27B-heretic-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf alexdenton/Qwen3.5-27B-heretic-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 "alexdenton/Qwen3.5-27B-heretic-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"

Docker Model Runner
How to use alexdenton/Qwen3.5-27B-heretic-GGUF with Docker Model Runner:
```
docker model run hf.co/alexdenton/Qwen3.5-27B-heretic-GGUF:Q4_K_M
```

Lemonade

How to use alexdenton/Qwen3.5-27B-heretic-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull alexdenton/Qwen3.5-27B-heretic-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-27B-heretic-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use alexdenton/Qwen3.5-27B-heretic-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 alexdenton/Qwen3.5-27B-heretic-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 alexdenton/Qwen3.5-27B-heretic-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

Qwen3.5-27B-heretic GGUF

GGUF quantizations of llmfan46/Qwen3.5-27B-heretic.

Available quantizations

File	Quant	Size	BPW	Quality
`Qwen3.5-27B-heretic-Q8_0.gguf`	Q8_0	27 GB	8.50	Excellent — near-lossless
`Qwen3.5-27B-heretic-Q6_K.gguf`	Q6_K	21 GB	6.57	Very good
`Qwen3.5-27B-heretic-Q5_K_M.gguf`	Q5_K_M	18 GB	5.77	Good
`Qwen3.5-27B-heretic-Q4_K_M.gguf`	Q4_K_M	15.4 GB	4.92	Recommended for most users
`Qwen3.5-27B-heretic-Q3_K_M.gguf`	Q3_K_M	12.4 GB	3.95	Usable — for low VRAM setups

Multimodal projector (vision)

File	Format	Size
`Qwen3.5-27B-mmproj-BF16.gguf`	BF16	889 MB

How to use

llama-server (recommended for API usage)

llama-server \
    -m Qwen3.5-27B-heretic-Q8_0.gguf \
    --mmproj Qwen3.5-27B-mmproj-BF16.gguf \
    --host 0.0.0.0 \
    --ctx-size 32768 \
    --gpu-layers 99 \
    --tensor-split 1,1 \
    --chat-template-kwargs '{"enable_thinking": false}' \
    --repeat-penalty 1.0 \
    --presence-penalty 1.5 \
    --min-p 0.0 \
    --top-k 20 \
    --top-p 0.8 \
    --temp 0.7

llama-cli

llama-cli -m Qwen3.5-27B-heretic-Q4_K_M.gguf -ngl 99 -c 4096

Which quantization should I use?

Q8_0: Best quality, needs ~28 GB VRAM. Fits on 2x RTX 3090.
Q6_K: Great quality, needs ~22 GB VRAM. Fits on 1x RTX 3090.
Q5_K_M: Good balance of quality and size. Fits on 1x RTX 3090 with room for context.
Q4_K_M: Most popular choice. Fits on 1x RTX 4070 Ti Super / RTX 3090.
Q3_K_M: For constrained VRAM setups (~13 GB needed).

Quantization details

Quantized with llama.cpp convert_hf_to_gguf.py + llama-quantize
Original model: llmfan46/Qwen3.5-27B-heretic

Downloads last month: 219

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

Inference Providers NEW

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Model tree for alexdenton/Qwen3.5-27B-heretic-GGUF

Base model

Qwen/Qwen3.5-27B

Finetuned

llmfan46/Qwen3.5-27B-uncensored-heretic-v1

Quantized

(9)

this model