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
sino commited on
Commit ·
5fb0bcb
1
Parent(s): ff4fdee
Update LMdecoder.py
Browse files- LMdecoder.py +2 -2
LMdecoder.py
CHANGED
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@@ -84,8 +84,8 @@ class LMDecoder(nn.Module):
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self.tokenizer = AutoTokenizer.from_pretrained(self.decoder_type)
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if self.tokenizer.pad_token is None:
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self.tokenizer.pad_token = self.tokenizer.eos_token
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self.LMconfig = AutoConfig.from_pretrained(self.decoder_type,
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self.lm = AutoModelForCausalLM.from_pretrained(self.decoder_type,
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def forward(self, input_ids, flatten_embs, attention_mask, labels, **kwargs):
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self.tokenizer = AutoTokenizer.from_pretrained(self.decoder_type)
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if self.tokenizer.pad_token is None:
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self.tokenizer.pad_token = self.tokenizer.eos_token
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self.LMconfig = AutoConfig.from_pretrained(self.decoder_type, token='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')
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self.lm = AutoModelForCausalLM.from_pretrained(self.decoder_type, token='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')
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def forward(self, input_ids, flatten_embs, attention_mask, labels, **kwargs):
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