Instructions to use Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF", dtype="auto")

llama-cpp-python

How to use Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF",
	filename="Qwen3.5-9B-DeepSeek-V4-Flash-BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-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 Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-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 Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-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 Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-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": "Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF:Q4_K_M

SGLang

How to use Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF with Ollama:
```
ollama run hf.co/Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF:Q4_K_M
```

Unsloth Studio new

How to use Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-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 Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-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 Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF to start chatting

Pi new

How to use Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-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": "Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-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 Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF with Docker Model Runner:
```
docker model run hf.co/Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF:Q4_K_M
```

Lemonade

How to use Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-9B-DeepSeek-V4-Flash-GGUF-Q4_K_M

List all available models

lemonade list

🌟 Qwen3.5-9B-DeepSeek-V4-Flash

💡 Model Overview & Design

Qwen3.5-9B-DeepSeek-V4-Flash is an efficient reasoning model distilled using high-quality data from DeepSeek-V4.

By leveraging the dataset Jackrong/DeepSeek-V4-Distill-8000x, this model successfully transfers the advanced structured reasoning and multi-step problem-solving capabilities of the DeepSeek-V4 architecture into the highly efficient Qwen3.5-9B parameter space.
This model was trained in an Unsloth environment, prioritizing stable gradient propagation and rigorous data curation to ensure the distillation process avoids merely learning "hollow chain-of-thought" and instead captures genuine logical generalization.

Designed for:

🧩 Structured Reasoning: Inheriting DeepSeek-V4's deep logic capabilities.
⚡ Flash Inference: Maintaining the token-efficiency and speed of the 9B parameter size.
🔧 Tool-augmented Workflows: Reliable agentic action generation.

🍎 About the Teacher Model: DeepSeek-V4

DeepSeek-V4 is the latest flagship open-source model series from DeepSeek, engineered for extreme efficiency, million-token long context (1M), and advanced Agentic workflows. As the source for this distillation, DeepSeek-V4 provides the high-fidelity reasoning signals necessary to push a 9B model beyond its architectural limits.

Key Technical Strengths of the Teacher Model:

🏆 World-Class Reasoning & Coding: DeepSeek-V4 demonstrates elite performance in mathematics (MATH-500), STEM subjects, and real-world software engineering (SWE-bench). Its "Think" modes provide the sophisticated Long-CoT (Chain-of-Thought) traces that define this model's logic.
🧠 Architectural Innovation: * Hybrid Attention & DSA: Features Token-level compression and DeepSeek Sparse Attention, which reduces KV Cache memory overhead by up to 90%, allowing for highly efficient long-context processing.
- Engram Memory & mHC: Utilizes Manifold-constrained Hyper-connections to decouple factual knowledge retrieval from dynamic logical reasoning, ensuring exceptional stability and generalization.
🤖 Agent-Centric Design: Specifically optimized for multi-step tool calling and complex environment interaction, ensuring that the distilled knowledge includes reliable "how-to-act" procedures, not just "how-to-talk."

By distilling from DeepSeek-V4-Flash, we have successfully mapped the high-density logic of a trillion-parameter class model onto the agile and high-speed Qwen3.5-9B framework.

🤝 Collaboration & Training Details

This model is the result of a close collaboration with hardware engineer Kyle Hessling. He generously provided the crucial compute equipment and managed both the rigorous post-training testing and continuous server maintenance. I want to express my gratitude to Kyle for his invaluable support! You can find him on X/Twitter here: @KyleHessling1

Training Infrastructure & Configuration:

🖥️ Hardware: NVIDIA DGX
💾 Training Data: DeepSeek-V4-Distill-8000x
🧪 Training Method: Distillation

🎯 Motivation & Distillation Insights

🧠 Latent Knowledge Activation: DeepSeek-V4's reasoning traces help the Qwen3.5-9B model activate its existing latent knowledge more effectively.
🏗️ Learning Procedures: The model learns actual problem-solving procedures, not just the output format.
🚀 Efficiency: The 8000x dataset provides a dense signal, allowing the 9B model to converge on reasoning tasks much faster than traditional large-scale SFT.

📊 Evaluation

This is an early controlled Q5_K_M comparison between Jackrong/Qwen3.5-9B-DeepSeek-V4-Flash and the official Qwen3.5-9B base model.

This evaluation was completed by Kyle Hessling, who ran the same evaluation suite twice under the same local inference conditions: once on the DeepSeek-V4 distill model and once on the official Qwen3.5-9B base model.

❤️ Special thanks to Kyle for the careful post-training testing and detailed comparison report. You can find him on X/Twitter here: @KyleHessling1.
📄 Full evaluation report: KyleHessling1/jackrong-deepseek-9b-eval.

🔬 Supporting Evidence

Recent work and empirical tests support this distillation approach:

Ren et al., 2026 — Rethinking Generalization in Reasoning SFT (arXiv:2604.06628)

The paper suggests that generalization in reasoning SFT is conditional. Key takeaways:

High-quality long-CoT data from DeepSeek-V4 enables cross-domain transfer.
Optimization Discipline: Short, highly-curated distillation (8000 examples) prevents the model from overfitting to the teacher's stylistic quirks while preserving the core reasoning engine.

🛠️ Best Practices

For optimal performance, we recommend the following generation parameters:

temperature=0.7 to 1.0 (Use lower temperature for strict coding tasks, higher for creative reasoning)
top_p=0.95

When interacting with the model, using a structured prompt template or standard ChatML format will yield the best reasoning results.

📚 Resources & Guides

👉 GitHub Repository: Jackrong-llm-finetuning-guide Visit the repository to dive into the codebase and reproduce the results locally or on Colab.

📥 Core Technical Document

🔗 Complete Fine-Tuning Guide (PDF)

A Note: My goal isn't just to detail a workflow, but to demystify LLM training. Beyond the social media hype, fine-tuning isn't an unattainable ritual—often, all you need is a Google account, a standard laptop, and relentless curiosity. All training and testing for this project were self-funded. If you find this model or guide helpful, a Star ⭐️ on GitHub would be the greatest encouragement. Thank you! 🙏

⚠️ Limitations

Parameter Constraints: While enhanced by DeepSeek-V4 distillation, the model is still bound by the 9B parameter limits and may struggle with extremely obscure knowledge.
Over-reasoning: On very simple queries, the model might still attempt to produce a lengthy reasoning chain due to the SFT bias.
Safety Trade-offs: Asymmetric gains mean that while reasoning improves, certain alignment-sensitive behaviors might regress.

🙏 Acknowledgements

Special thanks to:

DeepSeek Team for the foundational advancements in the V4 architecture.
Unsloth for efficient fine-tuning frameworks.
Open-source datasets and community contributors.
Researchers exploring reasoning SFT and distillation.

📖 Citation

@misc{jackrong_qwen35_9b_deepseek_v4_flash,
  title        = {Qwen3.5-9B-DeepSeek-V4-Flash},
  author       = {Jackrong},
  year         = {2026},
  publisher    = {Hugging Face}
}