Image-Text-to-Text
Transformers
Safetensors
qwen3_5
reasoning
thinking_modes
qwen3
grape
vision
multimodal
instruct
chat
coding
math
science
conversational
Instructions to use SL-AI/GRaPE-2.1-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SL-AI/GRaPE-2.1-Flash with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="SL-AI/GRaPE-2.1-Flash") 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("SL-AI/GRaPE-2.1-Flash") model = AutoModelForImageTextToText.from_pretrained("SL-AI/GRaPE-2.1-Flash") 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 SL-AI/GRaPE-2.1-Flash with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SL-AI/GRaPE-2.1-Flash" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SL-AI/GRaPE-2.1-Flash", "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/SL-AI/GRaPE-2.1-Flash
- SGLang
How to use SL-AI/GRaPE-2.1-Flash 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 "SL-AI/GRaPE-2.1-Flash" \ --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": "SL-AI/GRaPE-2.1-Flash", "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 "SL-AI/GRaPE-2.1-Flash" \ --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": "SL-AI/GRaPE-2.1-Flash", "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 SL-AI/GRaPE-2.1-Flash with Docker Model Runner:
docker model run hf.co/SL-AI/GRaPE-2.1-Flash
New Benchmark table!
Browse filesNew single benchmark table, please finish benchmarking GRaPE-2.1-Flash
README.md
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> **Note:** *Benchmarks are Underway for GRaPE 2.1 Flash, they will be empty and set as "TBD" for the time being*
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| Gemma-3-12B | 12B | 73.9 |
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| Qwen2.5-14B | 14B | 79.7 |
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### Mathematics — MATH (4-shot)
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| **GRaPE 2.1 Flash** | **9B** | **TBD** |
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| Qwen3-4B (Thinking) | 4B | 54.1 |
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| Qwen3-8B (Thinking) | 8B | ~65.0 |
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| Qwen2.5-7B-Instruct | 7B | 75.5 |
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| Qwen2.5-14B | 14B | 55.6 |
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| Gemma-3-12B | 12B | 44.4 |
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### Coding — EvalPlus (avg. HumanEval + MBPP)
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| **GRaPE 2.1 Flash** | **9B** | **TBD** |
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| Qwen3-4B-Instruct | 4B | 72.1 |
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| Qwen3-8B-Instruct | 8B | ~76.0 |
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| Qwen2.5-7B-Instruct | 7B | ~65.0 |
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| Gemma-3-12B | 12B | 52.7 |
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| Qwen2.5-14B | 14B | 60.7 |
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### Math Word Problems — GSM8K (4-shot)
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| Model | Params | GSM8K |
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| **GRaPE 2.1 Flash** | **9B** | **TBD** |
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| Qwen3-4B (Thinking) | 4B | 87.8 |
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| Qwen2.5-7B-Instruct | 7B | 91.1 |
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| Qwen2.5-14B | 14B | 90.2 |
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| Gemma-3-12B | 12B | 78.0 |
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***
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> **Note:** *Benchmarks are Underway for GRaPE 2.1 Flash, they will be empty and set as "TBD" for the time being*
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### Benchmarks
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| Models | Params | GPQA Diamond | MMLU-Pro | LiveCodeBench v6 | HMMT Nov 25 | TAU2-Bench | MultiChallenge |
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|----------------------|-------------------|--------------|----------|------------------|-------------|------------|----------------|
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| GRaPE 2.1 Flash | 9B | TBD | TBD | TBD | TBD | TBD | TBD |
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| GRM-2.5-Plus | 9B | 82.7 | 84.2 | 67.2 | 83.2 | 80.5 | 56.5 |
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| Qwen3.5-9B | 9B | 81.7 | 82.5 | 65.6 | 82.9 | 79.1 | 54.5 |
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| google/gemma-4-E4B-it| E4B (4.5B eff.) | 58.6 | 69.4 | 52.0 | -- | 42.2 | -- |
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***
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