Instructions to use OpenMOSS-Team/SciJudge-30B-2605 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenMOSS-Team/SciJudge-30B-2605 with Transformers:

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

pipe = pipeline("text-generation", model="OpenMOSS-Team/SciJudge-30B-2605")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenMOSS-Team/SciJudge-30B-2605")
model = AutoModelForCausalLM.from_pretrained("OpenMOSS-Team/SciJudge-30B-2605", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OpenMOSS-Team/SciJudge-30B-2605 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenMOSS-Team/SciJudge-30B-2605"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenMOSS-Team/SciJudge-30B-2605",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OpenMOSS-Team/SciJudge-30B-2605

SGLang

How to use OpenMOSS-Team/SciJudge-30B-2605 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 "OpenMOSS-Team/SciJudge-30B-2605" \
    --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": "OpenMOSS-Team/SciJudge-30B-2605",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "OpenMOSS-Team/SciJudge-30B-2605" \
        --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": "OpenMOSS-Team/SciJudge-30B-2605",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OpenMOSS-Team/SciJudge-30B-2605 with Docker Model Runner:
```
docker model run hf.co/OpenMOSS-Team/SciJudge-30B-2605
```

Mubuky commited on Jul 2

Commit

bd5cfa5

1 Parent(s): eb1640d

Update SciJudge-30B-2605 model card links and tags

Browse files

Files changed (1) hide show

README.md +3 -5

README.md CHANGED Viewed

@@ -6,12 +6,8 @@ base_model: Qwen/Qwen3-30B-A3B-Instruct-2507
 datasets:
 - OpenMOSS-Team/SciJudgeBench
 tags:
-- scientific-evaluation
-- citation-prediction
-- preference-learning
 - GRPO
-- moe
-- arxiv:2603.14473
 pipeline_tag: text-generation
 library_name: transformers
 model-index:
@@ -36,6 +32,8 @@ SciJudge-30B-2605 is a Qwen3-30B-A3B-Instruct-2507 MoE model fine-tuned for scie
 This release is part of [AI Can Learn Scientific Taste](https://arxiv.org/abs/2603.14473). The companion smaller model is [SciJudge-4B-2605](https://huggingface.co/OpenMOSS-Team/SciJudge-4B-2605), and the benchmark dataset is [SciJudgeBench](https://huggingface.co/datasets/OpenMOSS-Team/SciJudgeBench).
 ## Usage
 ```python

 datasets:
 - OpenMOSS-Team/SciJudgeBench
 tags:
+- scientific-taste
 - GRPO
 pipeline_tag: text-generation
 library_name: transformers
 model-index:
 This release is part of [AI Can Learn Scientific Taste](https://arxiv.org/abs/2603.14473). The companion smaller model is [SciJudge-4B-2605](https://huggingface.co/OpenMOSS-Team/SciJudge-4B-2605), and the benchmark dataset is [SciJudgeBench](https://huggingface.co/datasets/OpenMOSS-Team/SciJudgeBench).
+Resources: [Project page](https://tongjingqi.github.io/AI-Can-Learn-Scientific-Taste/) and [GitHub repository](https://github.com/tongjingqi/AI-Can-Learn-Scientific-Taste).
 ## Usage
 ```python