Text Generation
Transformers
Safetensors
English
gpt2
Generated from Trainer
text-generation-inference
Results
This model is a fine-tuned version of gpt2 on the Kubernetes dataset, which is updated in the same hub!
Model description
This model can be used to generate texts related to Kubernetes. This would be the first model towards interests in IBN.
Intended uses & limitations
It can be used for the text generation.
Training and evaluation data
This model contains only the training data and no evaluation data.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
Training Loss: TrainOutput(global_step=3, training_loss=3.4602108001708984, metrics={'train_runtime': 83.5107, 'train_samples_per_second': 0.036, 'train_steps_per_second': 0.036, 'total_flos': 1567752192000.0, 'train_loss': 3.4602108001708984, 'epoch': 3.0})
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
- Downloads last month
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Model tree for keethu/results
Base model
openai-community/gpt2
Install from pip and serve model
# Install vLLM from pip: pip install vllm# Start the vLLM server: vllm serve "keethu/results"# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "keethu/results", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'