Text Generation
Transformers
PyTorch
Safetensors
opt
Generated from Trainer
text-generation-inference
Instructions to use jda/opt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jda/opt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jda/opt")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jda/opt") model = AutoModelForCausalLM.from_pretrained("jda/opt") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use jda/opt with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jda/opt" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jda/opt", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jda/opt
- SGLang
How to use jda/opt 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 "jda/opt" \ --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": "jda/opt", "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 "jda/opt" \ --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": "jda/opt", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jda/opt with Docker Model Runner:
docker model run hf.co/jda/opt
update model card README.md
Browse files
README.md
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---
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license: other
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base_model: facebook/opt-350m
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tags:
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- generated_from_trainer
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model-index:
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The following hyperparameters were used during training:
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- learning_rate: 0.0005
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- train_batch_size:
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- eval_batch_size:
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 1000
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- num_epochs: 1
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- mixed_precision_training: Native AMP
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### Framework versions
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- Transformers 4.
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- Pytorch
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- Datasets 2.
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- Tokenizers 0.
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---
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license: other
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tags:
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- generated_from_trainer
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model-index:
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The following hyperparameters were used during training:
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- learning_rate: 0.0005
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- train_batch_size: 8
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- eval_batch_size: 64
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 1000
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- num_epochs: 1
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### Framework versions
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- Transformers 4.30.2
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- Pytorch 1.12.1
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- Datasets 2.13.2
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- Tokenizers 0.13.3
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