Instructions to use Jackrong/Qwopus3.5-9B-Coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jackrong/Qwopus3.5-9B-Coder with Transformers:

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

pipe = pipeline("image-text-to-text", model="Jackrong/Qwopus3.5-9B-Coder")
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 AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Jackrong/Qwopus3.5-9B-Coder")
model = AutoModelForImageTextToText.from_pretrained("Jackrong/Qwopus3.5-9B-Coder")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Jackrong/Qwopus3.5-9B-Coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jackrong/Qwopus3.5-9B-Coder"
# 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/Qwopus3.5-9B-Coder",
		"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/Qwopus3.5-9B-Coder

SGLang

How to use Jackrong/Qwopus3.5-9B-Coder 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/Qwopus3.5-9B-Coder" \
    --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/Qwopus3.5-9B-Coder",
		"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/Qwopus3.5-9B-Coder" \
        --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/Qwopus3.5-9B-Coder",
		"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"
						}
					}
				]
			}
		]
	}'

Unsloth Studio new

How to use Jackrong/Qwopus3.5-9B-Coder 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/Qwopus3.5-9B-Coder 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/Qwopus3.5-9B-Coder to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Jackrong/Qwopus3.5-9B-Coder to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Jackrong/Qwopus3.5-9B-Coder",
    max_seq_length=2048,
)

Docker Model Runner
How to use Jackrong/Qwopus3.5-9B-Coder with Docker Model Runner:
```
docker model run hf.co/Jackrong/Qwopus3.5-9B-Coder
```

Jackrong commited on 8 days ago

Commit

ca35421

verified ·

1 Parent(s): 29a1c09

Update README.md

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README.md +6 -1

README.md CHANGED Viewed

@@ -32,6 +32,10 @@ datasets:
 As the base model of this model, **Qwopus3.5-9B-v3.5** is already a model with powerful capabilities. On this foundation, **Qwopus3.5-9B-coder** is specially optimized and fine-tuned for high-performance Agentic Coding, complex Tool Calling, and deep logical reasoning.
 ![image](https://cdn-uploads.huggingface.co/production/uploads/66309bd090589b7c65950665/8qFQVuCxbgkWqKa2B_Vph.jpeg)
 > [!TIP]
@@ -50,6 +54,7 @@ This model is designed specifically for the following goals:
 - 🛠 More stable and accurate Tool Calling capabilities for terminal commands, file operations, and browsers
 - 🔁 Better cross-data source distillation alignment
 > [!WARNING]
 > - **Community Release Notice**: Qwopus3.5-9B-coder is released purely as an experimental community version, aiming to explore the combination of Agent capabilities and deep reasoning, and is only for research and exploration use.
 > - **Warning**: Because this model is vertically fine-tuned for programming agents and deep reasoning, and has not undergone comprehensive general performance evaluation, its capabilities in general domains or specific non-programming tasks may suffer from Capability Decay. Users are advised to be aware of its limitations in other scenarios while exploring its core capabilities.
@@ -70,7 +75,7 @@ To verify the execution efficiency and logical robustness of **Qwopus3.5-9B-code
 ### 🧪 Benchmark Results
 <div style="display: inline-block; padding: 6px 16px; background: #e0f2fe; color: #0369a1; border: 1px solid #bae6fd; border-radius: 8px; font-weight: 700; font-size: 16px; margin-bottom: 12px;">1. Complex Agent Performance - HermesAgent-20</div>
-The following is the comparative performance under the HermesAgent-20 (containing 20 complex agent scenarios) task set:
 <table style="width: 100%; border-collapse: collapse; font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif;">
   <thead>

 As the base model of this model, **Qwopus3.5-9B-v3.5** is already a model with powerful capabilities. On this foundation, **Qwopus3.5-9B-coder** is specially optimized and fine-tuned for high-performance Agentic Coding, complex Tool Calling, and deep logical reasoning.
+> 💡 **Why the 9B Dense Model?**
+> We believe that the 9B dense architecture represents the perfect **"sweet spot"** for large language models. It runs seamlessly at 8-bit precision on entry-level 16GB RAM devices—such as standard laptops and the Mac mini—making it exceptionally lightweight yet highly versatile. Without requiring expensive hardware, it allows you to achieve excellent performance paired with impressive inference speeds. Simply put, **Qwen3.5-9B is currently the best open-source model in its class.**
 ![image](https://cdn-uploads.huggingface.co/production/uploads/66309bd090589b7c65950665/8qFQVuCxbgkWqKa2B_Vph.jpeg)
 > [!TIP]
 - 🛠 More stable and accurate Tool Calling capabilities for terminal commands, file operations, and browsers
 - 🔁 Better cross-data source distillation alignment
 > [!WARNING]
 > - **Community Release Notice**: Qwopus3.5-9B-coder is released purely as an experimental community version, aiming to explore the combination of Agent capabilities and deep reasoning, and is only for research and exploration use.
 > - **Warning**: Because this model is vertically fine-tuned for programming agents and deep reasoning, and has not undergone comprehensive general performance evaluation, its capabilities in general domains or specific non-programming tasks may suffer from Capability Decay. Users are advised to be aware of its limitations in other scenarios while exploring its core capabilities.
 ### 🧪 Benchmark Results
 <div style="display: inline-block; padding: 6px 16px; background: #e0f2fe; color: #0369a1; border: 1px solid #bae6fd; border-radius: 8px; font-weight: 700; font-size: 16px; margin-bottom: 12px;">1. Complex Agent Performance - HermesAgent-20</div>
+The following is the comparative performance under the HermesAgent-20 task set:
 <table style="width: 100%; border-collapse: collapse; font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif;">
   <thead>