Instructions to use Kassadin88/Nemotron-9B-OpenCode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kassadin88/Nemotron-9B-OpenCode with Transformers:

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

pipe = pipeline("image-text-to-text", model="Kassadin88/Nemotron-9B-OpenCode")
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("Kassadin88/Nemotron-9B-OpenCode")
model = AutoModelForImageTextToText.from_pretrained("Kassadin88/Nemotron-9B-OpenCode")
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 Kassadin88/Nemotron-9B-OpenCode with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kassadin88/Nemotron-9B-OpenCode"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kassadin88/Nemotron-9B-OpenCode",
		"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/Kassadin88/Nemotron-9B-OpenCode

SGLang

How to use Kassadin88/Nemotron-9B-OpenCode 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 "Kassadin88/Nemotron-9B-OpenCode" \
    --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": "Kassadin88/Nemotron-9B-OpenCode",
		"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 "Kassadin88/Nemotron-9B-OpenCode" \
        --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": "Kassadin88/Nemotron-9B-OpenCode",
		"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"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Kassadin88/Nemotron-9B-OpenCode with Docker Model Runner:
```
docker model run hf.co/Kassadin88/Nemotron-9B-OpenCode
```

Kassadin88 commited on Apr 9

Commit

8afcaa2

verified ·

1 Parent(s): 9389c22

Add base model benchmarks and usage examples

Browse files

Files changed (1) hide show

README.md +75 -1

README.md CHANGED Viewed

@@ -61,7 +61,40 @@ response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special
 print(response)
 ```
-## 📊 Training Details
 The model was full-parameter fine-tuned from Qwen3.5-9B using DeepSpeed ZeRO3 with BF16 precision.
@@ -129,6 +162,47 @@ sampling_params = SamplingParams(
 outputs = llm.generate(prompts, sampling_params)
 ```
 ## ⚠️ Limitations
 - The model is primarily trained on code and may not perform well on general conversational tasks

 print(response)
 ```
+## 📊 Base Model Performance (Qwen3.5-9B)
+### Language Benchmarks
+| Category | Benchmark | Score |
+|----------|-----------|-------|
+| **Knowledge & STEM** | MMLU-Pro | 82.5 |
+| | MMLU-Redux | 91.1 |
+| | C-Eval | 88.2 |
+| | GPQA Diamond | 81.7 |
+| **Instruction Following** | IFEval | 91.5 |
+| | MultiChallenge | 54.5 |
+| **Long Context** | AA-LCR | 63.0 |
+| | LongBench v2 | 55.2 |
+| **Reasoning & Coding** | HMMT Feb 25 | 83.2 |
+| | LiveCodeBench v6 | 65.6 |
+| **Multilingualism** | MMMLU | 81.2 |
+| | MMLU-ProX | 76.3 |
+### Vision Language Benchmarks
+| Category | Benchmark | Score |
+|----------|-----------|-------|
+| **STEM and Puzzle** | MMMU | 78.4 |
+| | MathVision | 78.9 |
+| | Mathvista (mini) | 85.7 |
+| **General VQA** | RealWorldQA | 80.3 |
+| | MMStar | 79.7 |
+| **Document Understanding** | OmniDocBench1.5 | 87.7 |
+| | OCRBench | 89.2 |
+| **Video Understanding** | VideoMME (w/ sub) | 84.5 |
+| | MLVU | 84.4 |
+## 📈 Training Details
 The model was full-parameter fine-tuned from Qwen3.5-9B using DeepSpeed ZeRO3 with BF16 precision.
 outputs = llm.generate(prompts, sampling_params)
 ```
+### With SGLang
+```bash
+python -m sglang.launch_server \
+    --model-path Kassadin88/Nemotron-9B-OpenCode \
+    --port 8000 \
+    --tp-size 1 \
+    --context-length 16384
+```
+### OpenAI-Compatible API
+```python
+from openai import OpenAI
+client = OpenAI(
+    base_url="http://localhost:8000/v1",
+    api_key="EMPTY"
+)
+response = client.chat.completions.create(
+    model="Kassadin88/Nemotron-9B-OpenCode",
+    messages=[
+        {"role": "user", "content": "Write a quicksort implementation in Python"}
+    ],
+    max_tokens=512,
+    temperature=0.7,
+    top_p=0.9
+)
+print(response.choices[0].message.content)
+```
+## 🔧 Recommended Sampling Parameters
+| Task Type | Temperature | Top-p | Top-k |
+|-----------|-------------|-------|-------|
+| Code Generation | 0.3 | 0.95 | 20 |
+| Code Explanation | 0.7 | 0.9 | 20 |
+| Debugging | 0.5 | 0.95 | 20 |
+| General Tasks | 0.7 | 0.8 | 20 |
 ## ⚠️ Limitations
 - The model is primarily trained on code and may not perform well on general conversational tasks