Instructions to use internlm/Intern-S2-Preview-397B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use internlm/Intern-S2-Preview-397B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="internlm/Intern-S2-Preview-397B", trust_remote_code=True) 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)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForImageTextToText model = AutoModelForImageTextToText.from_pretrained("internlm/Intern-S2-Preview-397B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use internlm/Intern-S2-Preview-397B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "internlm/Intern-S2-Preview-397B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "internlm/Intern-S2-Preview-397B", "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/internlm/Intern-S2-Preview-397B
- SGLang
How to use internlm/Intern-S2-Preview-397B 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 "internlm/Intern-S2-Preview-397B" \ --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": "internlm/Intern-S2-Preview-397B", "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 "internlm/Intern-S2-Preview-397B" \ --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": "internlm/Intern-S2-Preview-397B", "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 internlm/Intern-S2-Preview-397B with Docker Model Runner:
docker model run hf.co/internlm/Intern-S2-Preview-397B
Add community evaluation results for Terminal-Bench 2.1
Browse filesThis PR adds community-provided evaluation results for the following benchmarks:
- **[Terminal-Bench 2.1](https://huggingface.co/datasets/harborframework/terminal-bench-2.1?eval_result=internlm/Intern-S2-Preview-397B&leaderboard_task_id=terminalbench_2_1)**
These results were extracted from Table 3 of the [paper](https://huggingface.co/papers/2608.13505). This is based on the new [evaluation results feature](https://huggingface.co/docs/hub/eval-results).
*Note: This is an automated PR. Please review the evaluation results before merging.*
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- dataset:
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id: harborframework/terminal-bench-2.1
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task_id: terminalbench_2_1
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value: 67.42
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source:
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url: https://arxiv.org/abs/2608.13505
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name: Paper (Table 3)
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