Instructions to use empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF", dtype="auto")

llama-cpp-python

How to use empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF",
	filename="Qwen3.5-9B-Claude-Opus-4.6-Distill-Q2_K.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill-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 empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill-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 empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill-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 empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF:Q4_K_M

Use Docker

docker model run hf.co/empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF with Ollama:
```
ollama run hf.co/empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF:Q4_K_M
```

Unsloth Studio new

How to use empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill-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 empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill-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 empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF to start chatting

Pi new

How to use empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill-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": "Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF with Docker Model Runner:
```
docker model run hf.co/empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF:Q4_K_M
```

Lemonade

How to use empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF / README.md

empero-ai

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	---
	license: apache-2.0
	language:
	- en
	base_model:
	- Qwen/Qwen3.5-9B
	library_name: transformers
	tags:
	- qwen
	- claude
	- opus
	- reasoning
	- distill
	datasets:
	- nohurry/Opus-4.6-Reasoning-3000x-filtered
	- Jackrong/Qwen3.5-reasoning-700x
	- TeichAI/claude-4.5-opus-high-reasoning-250x
	---
	# Qwen3.5-9B Claude Opus 4.6 Reasoning Distill — GGUF

	GGUF quantizations of [empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill](https://huggingface.co/empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill), a reasoning-focused fine-tune of [Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B).

	This model was trained to produce detailed chain-of-thought reasoning inside `<think>` tags before giving its final answer, distilled from Claude Opus 4.6 and Qwen3.5 reasoning traces.

	## Quantizations

	\| File \| Quant \| Size \| Description \|
	\|------\|-------\|------\|-------------\|
	\| `qwen3.5-9b-opus4.6-distill-Q2_K.gguf` \| Q2_K \| ~3.5 GB \| Smallest, lowest quality. For very constrained devices. \|
	\| `qwen3.5-9b-opus4.6-distill-Q3_K_M.gguf` \| Q3_K_M \| ~4.5 GB \| Low quality, usable for testing. \|
	\| `qwen3.5-9b-opus4.6-distill-Q4_K_M.gguf` \| Q4_K_M \| ~5.5 GB \| Recommended. Best balance of quality and size. \|
	\| `qwen3.5-9b-opus4.6-distill-Q5_K_M.gguf` \| Q5_K_M \| ~6.5 GB \| High quality, moderate size. \|
	\| `qwen3.5-9b-opus4.6-distill-Q6_K.gguf` \| Q6_K \| ~7.5 GB \| Very high quality, near-lossless. \|
	\| `qwen3.5-9b-opus4.6-distill-Q8_0.gguf` \| Q8_0 \| ~9.5 GB \| Highest quality quantization. \|
	\| `qwen3.5-9b-opus4.6-distill-f16.gguf` \| F16 \| ~18 GB \| Full precision, no quantization loss. \|

	For most users, Q4_K_M or Q5_K_M is the sweet spot.

	## How to Use

	### llama.cpp

	```bash
	llama-cli -m qwen3.5-9b-opus4.6-distill-Q5_K_M.gguf -p "<\|im_start\|>system\nYou are a deep reasoning AI. Think carefully inside <think> tags before answering.<\|im_end\|>\n<\|im_start\|>user\nExplain why the sky is blue.<\|im_end\|>\n<\|im_start\|>assistant\n" -n 2048
	```

	### Ollama

	```bash
	ollama run empero-ai/qwen3.5-9b-opus4.6-distill
	```

	### LM Studio / GPT4All / Jan

	Download the GGUF file of your choice and load it directly in the application.

	## Training Details

	### Method

	- Stage 1 — SFT (Supervised Fine-Tuning): 3 epochs on ~13K examples teaching the model the `<think>` reasoning format using QLoRA (4-bit, rank 64, alpha 128)
	- Base model: Qwen/Qwen3.5-9B
	- Hardware: RTX 5090 (32GB VRAM)
	- Attention: SDPA
	- Optimizer: Paged AdamW 8-bit
	- Learning rate: 1e-4 with cosine schedule
	- Effective batch size: 8 (batch 1 × gradient accumulation 8)
	- Max sequence length: 4096

	### SFT Results

	\| Metric \| Epoch 1 \| Epoch 2 (best) \| Epoch 3 \|
	\|--------\|---------\|-----------------\|---------\|
	\| Eval Loss \| 0.5205 \| 0.4809 \| 0.4915 \|
	\| Eval Token Accuracy \| 0.8494 \| 0.8615 \| 0.8617 \|
	\| Eval Entropy \| 0.508 \| 0.434 \| 0.394 \|

	Best checkpoint (epoch 2) was selected via `load_best_model_at_end`.

	### Datasets

	\| Dataset \| Examples \| Type \|
	\|---------\|----------\|------\|
	\| [nohurry/Opus-4.6-Reasoning-3000x-filtered](https://huggingface.co/datasets/nohurry/Opus-4.6-Reasoning-3000x-filtered) \| 2,326 \| Problem → thinking → solution \|
	\| [Jackrong/Qwen3.5-reasoning-700x](https://huggingface.co/datasets/Jackrong/Qwen3.5-reasoning-700x) \| 633 \| ShareGPT with `<think>` tags \|
	\| [TeichAI/claude-4.5-opus-high-reasoning-250x](https://huggingface.co/datasets/TeichAI/claude-4.5-opus-high-reasoning-250x) \| 250 \| Messages with `<think>` tags \|
	\| [Roman1111111/claude-opus-4.6-10000x](https://huggingface.co/datasets/Roman1111111/claude-opus-4.6-10000x) \| 9,631 \| Messages with reasoning traces \|
	\| Total \| 12,840 \| \|

	### Output Format

	The model outputs reasoning in `<think>` tags followed by its final answer:

	```
	<think>
	The user is asking about why the sky appears blue. This involves Rayleigh scattering...

	Sunlight contains all wavelengths of visible light. When it enters Earth's atmosphere,
	shorter wavelengths (blue/violet) scatter more than longer wavelengths (red/orange)...

	While violet actually scatters more than blue, our eyes are more sensitive to blue light,
	and some violet is absorbed by the upper atmosphere...
	</think>

	The sky appears blue due to Rayleigh scattering. When sunlight passes through Earth's
	atmosphere, the shorter blue wavelengths scatter in all directions more than the longer
	red wavelengths. Although violet light scatters even more, our eyes are more sensitive
	to blue, and some violet is absorbed higher in the atmosphere — so we perceive the sky
	as blue.
	```

	## About Empero AI

	This model was developed by [Empero AI](https://empero.org). We build open-source AI tools and models focused on advancing reasoning capabilities in smaller, efficient language models.

	## License

	This model inherits the [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) license from Qwen3.5-9B.