Instructions to use trl-internal-testing/tiny-MuseSparkForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use trl-internal-testing/tiny-MuseSparkForConditionalGeneration 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-MuseSparkForConditionalGeneration") 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("trl-internal-testing/tiny-MuseSparkForConditionalGeneration") model = AutoModelForMultimodalLM.from_pretrained("trl-internal-testing/tiny-MuseSparkForConditionalGeneration", 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 trl-internal-testing/tiny-MuseSparkForConditionalGeneration 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-MuseSparkForConditionalGeneration" # 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-MuseSparkForConditionalGeneration", "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-MuseSparkForConditionalGeneration
- SGLang
How to use trl-internal-testing/tiny-MuseSparkForConditionalGeneration 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-MuseSparkForConditionalGeneration" \ --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-MuseSparkForConditionalGeneration", "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-MuseSparkForConditionalGeneration" \ --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-MuseSparkForConditionalGeneration", "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-MuseSparkForConditionalGeneration with Docker Model Runner:
docker model run hf.co/trl-internal-testing/tiny-MuseSparkForConditionalGeneration
File size: 1,533 Bytes
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"backend": "tokenizers",
"bos_token": "<|begin_of_text|>",
"clean_up_tokenization_spaces": false,
"eos_token": "<|end_of_text|>",
"is_local": false,
"local_files_only": false,
"model_max_length": 1048576,
"pad_token": "<|finetune_right_pad|>",
"processor_class": "MuseSparkProcessor",
"response_template": {
"defaults": {
"role": "assistant"
},
"fields": {
"content": {
"close": [
"<|eot|>",
"<|eom|>"
],
"content": "text",
"join": "",
"open_pattern": "to=(?:user|commentary)<\\|message\\|>",
"repeats": true
},
"reasoning_content": {
"close": "<|eom|>",
"content": "text",
"open_pattern": "to=self<\\|message\\|>"
},
"tool_calls": {
"close": "</atem:invoke>",
"content": "xml-inline",
"content_args": {
"tag_pattern": "<atem:parameter\\b[^>]*?\\bname=\"(?P<key>[^\"]+)\"[^>]*?>(?P<value>.*?)</atem:parameter>",
"value_parser": {
"args": {
"allow_non_json": true
},
"name": "json"
}
},
"open_pattern": "<atem:invoke\\b[^>]*?\\bname=\"(?P<name>[^\"]+)\">",
"repeats": true,
"transform": {
"function": {
"arguments": "{content}",
"name": "{name}"
},
"type": "function"
}
}
},
"start_anchor": "<|start|>assistant"
},
"tokenizer_class": "TokenizersBackend"
}
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