Instructions to use Dzluck/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 Dzluck/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF with Transformers:

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Dzluck/Qwen3.5-9B-Claude-Opus-4.6-Distill-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 Dzluck/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Dzluck/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 serve -hf Dzluck/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Dzluck/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 Dzluck/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Dzluck/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 Dzluck/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Dzluck/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF:Q4_K_M

Use Docker

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

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

Unsloth Studio

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

How to use Dzluck/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 serve -hf Dzluck/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": "Dzluck/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use Dzluck/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Dzluck/Qwen3.5-9B-Claude-Opus-4.6-Distill-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 "Dzluck/Qwen3.5-9B-Claude-Opus-4.6-Distill-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 Dzluck/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF with Docker Model Runner:
```
docker model run hf.co/Dzluck/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF:Q4_K_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Dzluck/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

Hermes Agent

How to use Dzluck/Qwen3.5-9B-Claude-Opus-4.6-Distill-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 Dzluck/Qwen3.5-9B-Claude-Opus-4.6-Distill-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 Dzluck/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

FROM: empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF

GGUF quantizations of empero-ai/Qwen3.5-9B-Claude-Opus-4.6-Distill, a reasoning-focused fine-tune of 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

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

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	2,326	Problem → thinking → solution
Jackrong/Qwen3.5-reasoning-700x	633	ShareGPT with `<think>` tags
TeichAI/claude-4.5-opus-high-reasoning-250x	250	Messages with `<think>` tags
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. 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 license from Qwen3.5-9B.

Downloads last month: 294

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Inference Providers NEW

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Model tree for Dzluck/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Quantized

(435)

this model

Dzluck
/

Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF

Quantizations

How to Use

llama.cpp

Ollama

LM Studio / GPT4All / Jan

Training Details

Method

SFT Results

Datasets

Output Format

About Empero AI

License

Model tree for Dzluck/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF

Datasets used to train Dzluck/Qwen3.5-9B-Claude-Opus-4.6-Distill-GGUF