Text Generation
Transformers
PyTorch
TensorBoard
gpt2
Generated from Trainer
text-generation-inference
Instructions to use phinate/gpt2-med-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use phinate/gpt2-med-ft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="phinate/gpt2-med-ft")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("phinate/gpt2-med-ft") model = AutoModelForCausalLM.from_pretrained("phinate/gpt2-med-ft") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use phinate/gpt2-med-ft with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "phinate/gpt2-med-ft" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "phinate/gpt2-med-ft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/phinate/gpt2-med-ft
- SGLang
How to use phinate/gpt2-med-ft 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 "phinate/gpt2-med-ft" \ --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": "phinate/gpt2-med-ft", "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 "phinate/gpt2-med-ft" \ --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": "phinate/gpt2-med-ft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use phinate/gpt2-med-ft with Docker Model Runner:
docker model run hf.co/phinate/gpt2-med-ft
gpt2-med-ft
This model is a fine-tuned version of gpt2-medium on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.7011
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 17 | 2.0627 |
| No log | 2.0 | 34 | 1.8685 |
| No log | 3.0 | 51 | 1.7951 |
| No log | 4.0 | 68 | 1.7482 |
| No log | 5.0 | 85 | 1.7311 |
| No log | 6.0 | 102 | 1.7142 |
| No log | 7.0 | 119 | 1.7067 |
| No log | 8.0 | 136 | 1.7054 |
| No log | 9.0 | 153 | 1.7023 |
| No log | 10.0 | 170 | 1.7011 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
- Downloads last month
- 2