Image-Text-to-Text
Transformers
Safetensors
English
qwen2_5_vl
VLM
Computer-Use-Agent
OS-Agent
GUI
Grounding
conversational
Eval Results
text-generation-inference
Instructions to use Salesforce/GTA1-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Salesforce/GTA1-32B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Salesforce/GTA1-32B") 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, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Salesforce/GTA1-32B") model = AutoModelForMultimodalLM.from_pretrained("Salesforce/GTA1-32B", 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=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Salesforce/GTA1-32B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Salesforce/GTA1-32B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Salesforce/GTA1-32B", "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/Salesforce/GTA1-32B
- SGLang
How to use Salesforce/GTA1-32B 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 "Salesforce/GTA1-32B" \ --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": "Salesforce/GTA1-32B", "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 "Salesforce/GTA1-32B" \ --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": "Salesforce/GTA1-32B", "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 Salesforce/GTA1-32B with Docker Model Runner:
docker model run hf.co/Salesforce/GTA1-32B
File size: 5,785 Bytes
8e4436e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | {
"added_tokens_decoder": {
"151643": {"content": "[BOS]", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true},
"151644": {"content": "[EOS]", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true},
"151645": {"content": "<|im_end|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true},
"151646": {"content": "<|im_user|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true},
"151647": {"content": "<|im_assistant|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true},
"151648": {"content": "<|reserved_token_0|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true},
"151649": {"content": "<|start_header_id|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true},
"151650": {"content": "<|end_header_id|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true},
"151651": {"content": "<|reserved_token_1|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true},
"151652": {"content": "[EOT]", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true},
"151653": {"content": "<|im_system|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true},
"151654": {"content": "<|reserved_token_2|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true},
"151655": {"content": "<|reserved_token_3|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true},
"151656": {"content": "<|reserved_token_4|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true},
"151657": {"content": "<|reserved_token_5|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true},
"151658": {"content": "<|reserved_token_6|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true},
"151659": {"content": "<|reserved_token_7|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true},
"151660": {"content": "<|im_middle|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true},
"151661": {"content": "<|media_begin|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true},
"151662": {"content": "<|media_content|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true},
"151663": {"content": "<|media_end|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true},
"151664": {"content": "<|media_placeholder|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true},
"151665": {"content": "<|vision_start|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true},
"151666": {"content": "<|vision_end|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true},
"151667": {"content": "<|image_pad|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true},
"151668": {"content": "<|video_pad|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true},
"152062": {"content": "[UNK]", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true},
"152063": {"content": "[PAD]", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true}
},
"additional_special_tokens": [
"<|im_end|>", "<|im_user|>", "<|im_assistant|>",
"<|reserved_token_0|>", "<|start_header_id|>", "<|end_header_id|>",
"<|reserved_token_1|>", "[EOT]", "<|im_system|>",
"<|reserved_token_2|>", "<|reserved_token_3|>", "<|reserved_token_4|>",
"<|reserved_token_5|>", "<|reserved_token_6|>", "<|reserved_token_7|>",
"<|im_middle|>",
"<|media_begin|>", "<|media_content|>", "<|media_end|>", "<|media_placeholder|>",
"<|vision_start|>", "<|vision_end|>", "<|image_pad|>", "<|video_pad|>"
],
"bos_token": "[BOS]",
"clean_up_tokenization_spaces": false,
"eos_token": "[EOS]",
"extra_special_tokens": {},
"chat_template": "{%- for message in messages -%}{%- if loop.first and messages[0]['role'] != 'system' -%}{{'<|im_system|>system<|im_middle|>You are a helpful assistant<|im_end|>'}}{%- endif -%}{%- if message['role'] == 'system' -%}{{'<|im_system|>'}}{%- endif -%}{%- if message['role'] == 'user' -%}{{'<|im_user|>'}}{%- endif -%}{%- if message['role'] == 'assistant' -%}{{'<|im_assistant|>'}}{%- endif -%}{{- message['role'] -}}{{'<|im_middle|>'}}{%- if message['content'] is string -%}{{- message['content'] + '<|im_end|>' -}}{%- else -%}{%- for content in message['content'] -%}{%- if content['type'] == 'image' or 'image' in content or 'image_url' in content -%}{{'<|media_begin|>image<|media_content|><|media_placeholder|><|media_end|>'}}{%- else -%}{{content['text']}}{%- endif -%}{%- endfor -%}{{'<|im_end|>'}}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{'<|im_assistant|>assistant<|im_middle|>'}}{%- endif -%}",
"model_max_length": 1000000000000000019884624838656,
"pad_token": "[PAD]",
"tokenizer_class": "TikTokenV3",
"unk_token": "[UNK]",
"auto_map": {
"AutoTokenizer": ["tokenization_opencua.TikTokenV3", null]
}
}
|