Instructions to use trl-internal-testing/tiny-Qwen2_5_VLForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use trl-internal-testing/tiny-Qwen2_5_VLForConditionalGeneration 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-Qwen2_5_VLForConditionalGeneration") 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-Qwen2_5_VLForConditionalGeneration") model = AutoModelForImageTextToText.from_pretrained("trl-internal-testing/tiny-Qwen2_5_VLForConditionalGeneration") 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-Qwen2_5_VLForConditionalGeneration 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-Qwen2_5_VLForConditionalGeneration" # 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-Qwen2_5_VLForConditionalGeneration", "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-Qwen2_5_VLForConditionalGeneration
- SGLang
How to use trl-internal-testing/tiny-Qwen2_5_VLForConditionalGeneration 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-Qwen2_5_VLForConditionalGeneration" \ --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-Qwen2_5_VLForConditionalGeneration", "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-Qwen2_5_VLForConditionalGeneration" \ --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-Qwen2_5_VLForConditionalGeneration", "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-Qwen2_5_VLForConditionalGeneration with Docker Model Runner:
docker model run hf.co/trl-internal-testing/tiny-Qwen2_5_VLForConditionalGeneration
Upload Qwen2_5_VLForConditionalGeneration
Browse files- config.json +4 -4
- generation_config.json +1 -1
- model.safetensors +1 -1
config.json
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size":
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"image_token_id": 151655,
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"initializer_range": 0.02,
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"intermediate_size": 11008,
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"max_position_embeddings": 128000,
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"max_window_layers": 70,
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"model_type": "qwen2_5_vl",
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"num_attention_heads":
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"num_hidden_layers":
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"num_key_value_heads": 2,
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"rms_norm_eps": 1e-06,
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"rope_scaling": {
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"vision_token_id": 151654,
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"vocab_size": 151936
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},
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"transformers_version": "4.
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"use_cache": true,
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"use_sliding_window": false,
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"video_token_id": 151656,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 16,
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"image_token_id": 151655,
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"initializer_range": 0.02,
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"intermediate_size": 11008,
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"max_position_embeddings": 128000,
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"max_window_layers": 70,
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"model_type": "qwen2_5_vl",
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"num_attention_heads": 4,
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"num_hidden_layers": 2,
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"num_key_value_heads": 2,
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"rms_norm_eps": 1e-06,
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"rope_scaling": {
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"vision_token_id": 151654,
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"vocab_size": 151936
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"transformers_version": "4.56.1",
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"use_cache": true,
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"use_sliding_window": false,
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"video_token_id": 151656,
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generation_config.json
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"_from_model_config": true,
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"bos_token_id": 151643,
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"transformers_version": "4.
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}
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"_from_model_config": true,
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"bos_token_id": 151643,
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"eos_token_id": 151645,
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"transformers_version": "4.56.1"
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}
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model.safetensors
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size 18086192
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