Instructions to use review-artifacts/nsdl-3b-sft-locus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use review-artifacts/nsdl-3b-sft-locus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="review-artifacts/nsdl-3b-sft-locus") 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 AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("review-artifacts/nsdl-3b-sft-locus") model = AutoModelForMultimodalLM.from_pretrained("review-artifacts/nsdl-3b-sft-locus", device_map="auto") 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=256) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use review-artifacts/nsdl-3b-sft-locus with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "review-artifacts/nsdl-3b-sft-locus" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "review-artifacts/nsdl-3b-sft-locus", "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/review-artifacts/nsdl-3b-sft-locus
- SGLang
How to use review-artifacts/nsdl-3b-sft-locus 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 "review-artifacts/nsdl-3b-sft-locus" \ --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": "review-artifacts/nsdl-3b-sft-locus", "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 "review-artifacts/nsdl-3b-sft-locus" \ --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": "review-artifacts/nsdl-3b-sft-locus", "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 review-artifacts/nsdl-3b-sft-locus with Docker Model Runner:
docker model run hf.co/review-artifacts/nsdl-3b-sft-locus
NSDL 3B (SFT), LOCUS: anonymized checkpoint for double-anonymous review
Row "Table 2: NSDL 3B (SFT)" of the submission. Weights are stored in bfloat16, the dtype the
evaluation loader uses. Load and run with the code in the anonymized repository
linked from the paper (nsdl/ + scripts/eval_lnsdl_agentic_viki_l2.py);
evaluation flags are in that repository's README.
Trained on the LOCUS train split with the VLM input capped at 524288 pixels (seed 1234).
Provided for peer review only; do not redistribute during review. Author,
affiliation and citation information is intentionally omitted.
| File | Bytes | sha256 |
|---|---|---|
added_tokens.json |
605 | 58b54bbe36fc752f79a24a271ef66a0a0830054b4dfad94bde757d851968060b |
chat_template.json |
1049 | 94174d7176c52a7192f96fc34eb2cf23c7c2059d63cdbfadca1586ba89731fb7 |
config.json |
1540 | 236e39592c9ba9eb4db6ef70b7df4eb9201eebae234b6f8e915d461c0ee18ad9 |
generation_config.json |
290 | d04905081373c40b5cf0dc0ab432c879b8312bea794b1fd14c4c8828054d1409 |
merges.txt |
1671853 | 8831e4f1a044471340f7c0a83d7bd71306a5b867e95fd870f74d0c5308a904d5 |
model-00001-of-00002.safetensors |
5351163000 | 02169e25a681f014f1da9b1f973712103e35163e00da91f6556a9cdad2b12c8d |
model-00002-of-00002.safetensors |
2780505008 | 1e1b7f9d4b390c3da7d04a5bbdd6b40ad05a658a80ac9ec6a9a16d9197a80280 |
model.safetensors.index.json |
65505 | 37521b6b34cd7a6c8fcdda655a14fc648722a5ec110788179d1d55847ba5ac45 |
preprocessor_config.json |
571 | 10244222141516cc6ac28c41435d5ced7db23d352ce67ae588d5b248ccbc0b2f |
special_tokens_map.json |
613 | 76862e765266b85aa9459767e33cbaf13970f327a0e88d1c65846c2ddd3a1ecd |
tokenizer.json |
11421995 | 33624f49f1034c4f5d92e3ec47ccdf80ccd02caf1eb1d769872ba7fb1e5be112 |
tokenizer_config.json |
7300 | c182cc5f590489eba7c5435864b0814fcd9256ef72933f8ccc142116974dbb01 |
vocab.json |
2776833 | ca10d7e9fb3ed18575dd1e277a2579c16d108e32f27439684afa0e10b1440910 |
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