Instructions to use Crownelius/Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Crownelius/Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Crownelius/Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5",
	filename="Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5.BF16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Crownelius/Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Crownelius/Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Crownelius/Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Crownelius/Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Crownelius/Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5: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 Crownelius/Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Crownelius/Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5: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 Crownelius/Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Crownelius/Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5:Q4_K_M

Use Docker

docker model run hf.co/Crownelius/Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5:Q4_K_M

LM Studio
Jan
Ollama
How to use Crownelius/Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5 with Ollama:
```
ollama run hf.co/Crownelius/Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5:Q4_K_M
```

Unsloth Studio

How to use Crownelius/Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5 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 Crownelius/Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5 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 Crownelius/Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Crownelius/Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5 to start chatting

How to use Crownelius/Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Crownelius/Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5: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": "Crownelius/Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Crownelius/Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Crownelius/Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5: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 Crownelius/Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Crownelius/Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5 with Docker Model Runner:
```
docker model run hf.co/Crownelius/Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5:Q4_K_M
```

Lemonade

How to use Crownelius/Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Crownelius/Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5:Q4_K_M

Run and chat with the model

lemonade run user.Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5-Q4_K_M

List all available models

lemonade list

🪶 CROW-4B

Flagship Intelligence. Featherweight Footprint. Meticulously distilled from Claude Opus 4.6 into a highly efficient Qwen 3.5 architecture.

Architecture: Qwen 3.5 | Parameters: 4 Billion | Teacher Model: Claude Opus 4.6 | Type: Distilled LLM

🌟 Model Highlights

Distilled Excellence: Captures the deep reasoning, nuanced formatting, and instruction-following capabilities of Claude Opus 4.6.
Ultra Compact: At just 4B parameters, Crow-4B runs on virtually any consumer GPU or CPU — including laptops and edge devices — without sacrificing contextual depth.
Qwen 3.5 Backbone: Inherits robust multilingual support, a massive context window, and structural stability.
Broadest Training Mix: Trained on 15 datasets spanning reasoning, creative writing, agentic coding, security research, and roleplay — over 25,000 carefully curated examples.

---Generating this model was expensive. You can support this one and future models by tipping. https://ko-fi.com/abcuo

Available Model Files:

Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5.BF16.gguf
Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5.Q8_0.gguf
Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5.Q5_K_M.gguf
Crow-4B-Opus-4.6-Distill-Heretic_Qwen3.5.Q4_K_M.gguf

Training Details


Base Model	`tvall43/Qwen3.5-4B-heretic`
Training Framework	Unsloth + TRL SFTTrainer
LoRA Rank	r=32, α=32
Learning Rate	2e-4 (cosine schedule)
Epochs	1
Max Sequence Length	2048 (model max)
Hardware	NVIDIA A100 40GB

Available Model files:

Datasets (15 total, ~25,500 examples)

#	Dataset	Category
1	`crownelius/Opus4.6-No-Reasoning-260x`	Reasoning
2	`crownelius/Opus-4.6-Reasoning-2100x-formatted`	Reasoning
3	`crownelius/Creative_Writing_Multiturn_Enhanced`	Creative Writing
4	`peteromallet/dataclaw-peteromallet`	Agentic
5	`TeichAI/Pony-Alpha-15k`	Creative / Roleplay
6	`crownelius/Pony-Alpha-3000x-formatted`	Creative / Roleplay
7	`HuggingFaceH4/llava-instruct-mix-vsft`	Instruction Following
8	`reedmayhew/LittleMonster`	Uncensored
9	`tandevllc/hacking-tricks`	Security Research
10	`m-a-p/Code-Feedback`	Coding
11	`WithinUsAI/Python_GOD_Coder_50k`	Coding
12	`peteromallet/my-personal-codex-data`	Agentic / Coding
13	`misterkerns/my-personal-claude-code-data`	Agentic / Coding
14	`Roman1111111/gemini-3-pro-10000x-hard-high-reasoning`	Reasoning
15	`reedmayhew/gemini-3.1-pro-2048-reasoning-1100x`	Coding + Reasoning