Instructions to use trl-internal-testing/tiny-LlavaForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use trl-internal-testing/tiny-LlavaForConditionalGeneration with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="trl-internal-testing/tiny-LlavaForConditionalGeneration") 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("trl-internal-testing/tiny-LlavaForConditionalGeneration") model = AutoModelForImageTextToText.from_pretrained("trl-internal-testing/tiny-LlavaForConditionalGeneration") 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 trl-internal-testing/tiny-LlavaForConditionalGeneration with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "trl-internal-testing/tiny-LlavaForConditionalGeneration" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "trl-internal-testing/tiny-LlavaForConditionalGeneration", "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/trl-internal-testing/tiny-LlavaForConditionalGeneration
- SGLang
How to use trl-internal-testing/tiny-LlavaForConditionalGeneration 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 "trl-internal-testing/tiny-LlavaForConditionalGeneration" \ --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": "trl-internal-testing/tiny-LlavaForConditionalGeneration", "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 "trl-internal-testing/tiny-LlavaForConditionalGeneration" \ --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": "trl-internal-testing/tiny-LlavaForConditionalGeneration", "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 trl-internal-testing/tiny-LlavaForConditionalGeneration with Docker Model Runner:
docker model run hf.co/trl-internal-testing/tiny-LlavaForConditionalGeneration
Upload processor
Browse files- added_tokens.json +4 -0
- chat_template.json +3 -0
- preprocessor_config.json +28 -0
- processor_config.json +7 -0
- special_tokens_map.json +31 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +64 -0
added_tokens.json
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{
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"<image>": 32000,
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"<pad>": 32001
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}
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chat_template.json
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{
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"chat_template": "{% for message in messages %}{% if message['role'] != 'system' %}{{ message['role'].upper() + ': '}}{% endif %}{# Render all images first #}{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}{{ '<image>\n' }}{% endfor %}{# Render all text next #}{% if message['role'] != 'assistant' %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{{ content['text'] + ' '}}{% endfor %}{% else %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{% generation %}{{ content['text'] + ' '}}{% endgeneration %}{% endfor %}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'ASSISTANT:' }}{% endif %}"
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}
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preprocessor_config.json
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{
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"crop_size": {
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"height": 336,
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"width": 336
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},
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"do_center_crop": true,
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"do_convert_rgb": true,
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"do_normalize": true,
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"do_rescale": true,
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"do_resize": true,
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"image_mean": [
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0.48145466,
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0.4578275,
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0.40821073
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],
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"image_processor_type": "CLIPImageProcessor",
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"image_std": [
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0.26862954,
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0.26130258,
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0.27577711
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],
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"processor_class": "LlavaProcessor",
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"resample": 3,
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"rescale_factor": 0.00392156862745098,
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"size": {
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"shortest_edge": 336
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}
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}
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processor_config.json
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{
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"image_token": "<image>",
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"num_additional_image_tokens": 1,
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"patch_size": 14,
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"processor_class": "LlavaProcessor",
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"vision_feature_select_strategy": "default"
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}
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special_tokens_map.json
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{
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"bos_token": {
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"content": "<s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"image_token": "<image>",
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"pad_token": {
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"content": "<pad>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.json
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tokenizer.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
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size 499723
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tokenizer_config.json
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{
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"add_bos_token": true,
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"add_eos_token": false,
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"add_prefix_space": null,
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"added_tokens_decoder": {
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"0": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"1": {
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"content": "<s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"2": {
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"32000": {
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"content": "<image>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"32001": {
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"content": "<pad>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"bos_token": "<s>",
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"clean_up_tokenization_spaces": false,
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"eos_token": "</s>",
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"extra_special_tokens": {
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"image_token": "<image>"
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},
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"image_token": "<image>",
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"legacy": false,
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "<pad>",
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"padding_side": "left",
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"processor_class": "LlavaProcessor",
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"sp_model_kwargs": {},
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"tokenizer_class": "LlamaTokenizer",
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"trust_remote_code": false,
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"unk_token": "<unk>",
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"use_default_system_prompt": false
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}
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