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 7 days ago

Commit

d3a3bb0

verified ·

1 Parent(s): 1e87f04

Update README.md

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Files changed (1) hide show

README.md +47 -0

README.md CHANGED Viewed

@@ -191,6 +191,53 @@ BugFind-15 is a test set containing 15 scenarios from shallow to deep, aiming to
   </tbody>
 </table>
 > [!IMPORTANT]

   </tbody>
 </table>
+### 🪐 SWE-bench Verified Performance (Repository-level Coding Capability)
+The following shows the comparative performance on **SWE-bench Verified**, which evaluates language models on resolving software engineering issues in real-world open-source repositories:
+<table style="width: 100%; border-collapse: collapse; font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif;">
+  <thead>
+    <tr>
+      <td colspan="3" style="padding: 8px 12px; font-weight: 600; color: #7c3aed; border-bottom: 1px solid rgba(124, 58, 237, 0.2); background: rgba(124, 58, 237, 0.05);">SWE-bench Verified Performance Metrics</td>
+    </tr>
+    <tr style="background: rgba(128, 128, 128, 0.02);">
+      <th style="padding: 7px 7px; padding-left: 20px; text-align: left; border-bottom: 1px solid rgba(128, 128, 128, 0.15); font-size: 13px; color: #666;">Model</th>
+      <th style="padding: 7px 7px; text-align: center; border-bottom: 1px solid rgba(128, 128, 128, 0.15); font-size: 13px; color: #666;">Test Set</th>
+      <th style="padding: 7px 7px; text-align: center; border-bottom: 1px solid rgba(128, 128, 128, 0.15); font-size: 13px; color: #666;">Comprehensive Score (%)</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td style="padding: 7px 7px; padding-left: 20px; border-bottom: 1px solid rgba(128, 128, 128, 0.15);"><span style="color: #666;">Claude 4.5 Opus</span></td>
+      <td style="padding: 7px 7px; text-align: center; border-bottom: 1px solid rgba(128, 128, 128, 0.15);">SWE-bench Verified</td>
+      <td style="padding: 7px 7px; text-align: center; border-bottom: 1px solid rgba(128, 128, 128, 0.15);">80.9</td>
+    </tr>
+    <tr>
+      <td style="padding: 7px 7px; padding-left: 20px; border-bottom: 1px solid rgba(128, 128, 128, 0.15);"><a href="https://huggingface.co/Qwen/Qwen3.5-27B" style="color: #666; text-decoration: none;">Qwen/Qwen3.5-27B</a></td>
+      <td style="padding: 7px 7px; text-align: center; border-bottom: 1px solid rgba(128, 128, 128, 0.15);">SWE-bench Verified</td>
+      <td style="padding: 7px 7px; text-align: center; border-bottom: 1px solid rgba(128, 128, 128, 0.15);">75.0</td>
+    </tr>
+    <tr>
+      <td style="padding: 7px 7px; padding-left: 20px; border-bottom: 1px solid rgba(128, 128, 128, 0.15);"><a href="https://huggingface.co/Qwen/Qwen3.6-35B-A3B" style="color: #666; text-decoration: none;">Qwen/Qwen3.6-35B-A3B</a></td>
+      <td style="padding: 7px 7px; text-align: center; border-bottom: 1px solid rgba(128, 128, 128, 0.15);">SWE-bench Verified</td>
+      <td style="padding: 7px 7px; text-align: center; border-bottom: 1px solid rgba(128, 128, 128, 0.15);">73.4</td>
+    </tr>
+    <tr>
+      <td style="padding: 7px 7px; padding-left: 20px; border-bottom: 1px solid rgba(128, 128, 128, 0.15);"><b><a href="https://huggingface.co/Jackrong/Qwopus3.5-9B-coder-GGUF" style="color: #7c3aed; text-decoration: none;">Qwopus3.5-9B-coder</a></b></td>
+      <td style="padding: 7px 7px; text-align: center; border-bottom: 1px solid rgba(128, 128, 128, 0.15);">SWE-bench Verified</td>
+      <td style="padding: 7px 7px; text-align: center; border-bottom: 1px solid rgba(128, 128, 128, 0.15); color: #7c3aed; font-weight: bold;">53.33</td>
+    </tr>
+    <tr>
+      <td style="padding: 7px 7px; padding-left: 20px; border-bottom: 1px solid rgba(128, 128, 128, 0.15);"><a href="https://huggingface.co/google/gemma-4-31B-it" style="color: #666; text-decoration: none;">google/gemma-4-31B-it</a></td>
+      <td style="padding: 7px 7px; text-align: center; border-bottom: 1px solid rgba(128, 128, 128, 0.15);">SWE-bench Verified</td>
+      <td style="padding: 7px 7px; text-align: center; border-bottom: 1px solid rgba(128, 128, 128, 0.15);">52.0</td>
+    </tr>
+    <tr>
+      <td style="padding: 7px 7px; padding-left: 20px; border-bottom: 1px solid rgba(128, 128, 128, 0.15);"><span style="color: #666;">google/gemma-4-26B-A4B</span></td>
+      <td style="padding: 7px 7px; text-align: center; border-bottom: 1px solid rgba(128, 128, 128, 0.15);">SWE-bench Verified</td>
+      <td style="padding: 7px 7px; text-align: center; border-bottom: 1px solid rgba(128, 128, 128, 0.15);">45.0 - 48.0</td>
+    </tr>
+  </tbody>
+</table>
 > [!IMPORTANT]