Text Generation
Transformers
PyTorch
English
MAELM
feature-extraction
audio2text
music2text
musicllm
music foundation model
custom_code
Instructions to use UniMus/OpenJMLA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UniMus/OpenJMLA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="UniMus/OpenJMLA", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("UniMus/OpenJMLA", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use UniMus/OpenJMLA with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "UniMus/OpenJMLA" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "UniMus/OpenJMLA", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/UniMus/OpenJMLA
- SGLang
How to use UniMus/OpenJMLA 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 "UniMus/OpenJMLA" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "UniMus/OpenJMLA", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "UniMus/OpenJMLA" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "UniMus/OpenJMLA", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use UniMus/OpenJMLA with Docker Model Runner:
docker model run hf.co/UniMus/OpenJMLA
Update README.md
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README.md
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gen_ids = model.forward_test(input)
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gen_text = model.neck.tokenizer.batch_decode(gen_ids.clip(0))
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# 4. Post-processing
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gen_text = gen_text.split('<s>')[-1].split('\n')[0].strip()
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gen_text = gen_text.replace(' in Chinese','')
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gen_text = gen_text.replace(' Chinese','')
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gen_ids = model.forward_test(input)
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gen_text = model.neck.tokenizer.batch_decode(gen_ids.clip(0))
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# 4. Post-processing
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# Given that the training data may contain biases, the generated texts might need some straightforward post-processing to ensure accuracy.
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# In future versions, we will enhance the quality of the data.
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gen_text = gen_text.split('<s>')[-1].split('\n')[0].strip()
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gen_text = gen_text.replace(' in Chinese','')
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gen_text = gen_text.replace(' Chinese','')
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