Text Generation
Transformers
PyTorch
TensorBoard
gpt2
Generated from Trainer
text-generation-inference
Instructions to use ECE1786-AG/lyrics-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ECE1786-AG/lyrics-generator with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ECE1786-AG/lyrics-generator")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ECE1786-AG/lyrics-generator") model = AutoModelForCausalLM.from_pretrained("ECE1786-AG/lyrics-generator") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use ECE1786-AG/lyrics-generator with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ECE1786-AG/lyrics-generator" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ECE1786-AG/lyrics-generator", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ECE1786-AG/lyrics-generator
- SGLang
How to use ECE1786-AG/lyrics-generator 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 "ECE1786-AG/lyrics-generator" \ --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": "ECE1786-AG/lyrics-generator", "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 "ECE1786-AG/lyrics-generator" \ --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": "ECE1786-AG/lyrics-generator", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ECE1786-AG/lyrics-generator with Docker Model Runner:
docker model run hf.co/ECE1786-AG/lyrics-generator
lyrics-generator-v2
This model is a fine-tuned version of gpt2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.1114
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.4325 | 0.36 | 200 | 2.3267 |
| 2.4121 | 0.71 | 400 | 2.2693 |
| 2.3582 | 1.07 | 600 | 2.2401 |
| 2.2903 | 1.42 | 800 | 2.2182 |
| 2.2738 | 1.78 | 1000 | 2.2046 |
| 2.2322 | 2.14 | 1200 | 2.1922 |
| 2.1933 | 2.49 | 1400 | 2.1832 |
| 2.1944 | 2.85 | 1600 | 2.1736 |
| 2.1632 | 3.2 | 1800 | 2.1648 |
| 2.1366 | 3.56 | 2000 | 2.1554 |
| 2.1492 | 3.91 | 2200 | 2.1491 |
| 2.1108 | 4.27 | 2400 | 2.1472 |
| 2.0882 | 4.63 | 2600 | 2.1422 |
| 2.0971 | 4.98 | 2800 | 2.1343 |
| 2.0829 | 5.34 | 3000 | 2.1318 |
| 2.042 | 5.69 | 3200 | 2.1280 |
| 2.0375 | 6.05 | 3400 | 2.1261 |
| 2.0146 | 6.41 | 3600 | 2.1245 |
| 2.0551 | 6.76 | 3800 | 2.1217 |
| 1.992 | 7.12 | 4000 | 2.1182 |
| 1.9994 | 7.47 | 4200 | 2.1170 |
| 2.0189 | 7.83 | 4400 | 2.1156 |
| 1.9795 | 8.19 | 4600 | 2.1133 |
| 2.0101 | 8.54 | 4800 | 2.1143 |
| 1.9864 | 8.9 | 5000 | 2.1111 |
| 1.9602 | 9.25 | 5200 | 2.1120 |
| 1.9899 | 9.61 | 5400 | 2.1117 |
| 1.9928 | 9.96 | 5600 | 2.1114 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
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