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 Settings

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

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 about 11 hours ago

Commit

e55d2e2

verified ·

1 Parent(s): e5a0c52

Update README.md

Browse files

Files changed (1) hide show

README.md +2 -5

README.md CHANGED Viewed

@@ -249,10 +249,10 @@ The following shows the comparative performance on **SWE-bench Verified**, which
 > - ❤️ **Kyle Hessling** for his generous hardware and equipment support. You can follow him for more updates on X / Twitter: [@KyleHessling1](https://x.com/KyleHessling1).
 ---
 #### Community-Reported Tool Calling Evaluation
-A community user independently evaluated Qwopus3.5-9B-coder on a tool-recall test with up to 31 available tools and an adversarial tool-selection phase containing semantic overlaps and decoy tools.
 | Model | Phase 1: Tool Recall | Phase 2: Adversarial Tool Selection |
 |---|---:|---:|
@@ -260,9 +260,6 @@ A community user independently evaluated Qwopus3.5-9B-coder on a tool-recall tes
 | Claude Opus 4.6 | 100% | 27 / 28 (96%) |
 | Qwen3.5-9B | 100% | 26 / 28 (93%) |
-> This is an unofficial community-reported evaluation rather than a standardized benchmark. See the original Hugging Face discussion for the full test logs and additional model comparisons.
 ---
 ### 🧪 Core Dataset Usage: Trace Inversion and High-Quality Agent Traces

 > - ❤️ **Kyle Hessling** for his generous hardware and equipment support. You can follow him for more updates on X / Twitter: [@KyleHessling1](https://x.com/KyleHessling1).
 ---
 #### Community-Reported Tool Calling Evaluation
+> [!TIP]
+> A community user independently evaluated Qwopus3.5-9B-coder on a tool-recall test with up to 31 available tools and an adversarial tool-selection phase containing semantic overlaps and decoy tools.
 | Model | Phase 1: Tool Recall | Phase 2: Adversarial Tool Selection |
 |---|---:|---:|
 | Claude Opus 4.6 | 100% | 27 / 28 (96%) |
 | Qwen3.5-9B | 100% | 26 / 28 (93%) |
 ---
 ### 🧪 Core Dataset Usage: Trace Inversion and High-Quality Agent Traces