Text Generation
Transformers
PyTorch
TensorBoard
Safetensors
gpt2
Generated from Trainer
text-generation-inference
Instructions to use juancopi81/GPT-Y with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use juancopi81/GPT-Y with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="juancopi81/GPT-Y")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("juancopi81/GPT-Y") model = AutoModelForCausalLM.from_pretrained("juancopi81/GPT-Y") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use juancopi81/GPT-Y with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "juancopi81/GPT-Y" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "juancopi81/GPT-Y", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/juancopi81/GPT-Y
- SGLang
How to use juancopi81/GPT-Y 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 "juancopi81/GPT-Y" \ --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": "juancopi81/GPT-Y", "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 "juancopi81/GPT-Y" \ --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": "juancopi81/GPT-Y", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use juancopi81/GPT-Y with Docker Model Runner:
docker model run hf.co/juancopi81/GPT-Y
GPT-Y
This model is a fine-tuned version of juancopi81/gpt2-finetuned-yannic-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 3.0797
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: 2e-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: 8
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 403 | 3.0809 |
| 2.9847 | 2.0 | 806 | 3.0811 |
| 2.9516 | 3.0 | 1209 | 3.0781 |
| 2.916 | 4.0 | 1612 | 3.0791 |
| 2.9006 | 5.0 | 2015 | 3.0775 |
| 2.9006 | 6.0 | 2418 | 3.0799 |
| 2.8814 | 7.0 | 2821 | 3.0798 |
| 2.8672 | 8.0 | 3224 | 3.0797 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
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