Text Generation
Transformers
TensorBoard
Safetensors
gpt2
Generated from Trainer
text-generation-inference
Instructions to use Anish13/pretrained_gpt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Anish13/pretrained_gpt2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Anish13/pretrained_gpt2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Anish13/pretrained_gpt2") model = AutoModelForCausalLM.from_pretrained("Anish13/pretrained_gpt2") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Anish13/pretrained_gpt2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Anish13/pretrained_gpt2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Anish13/pretrained_gpt2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Anish13/pretrained_gpt2
- SGLang
How to use Anish13/pretrained_gpt2 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 "Anish13/pretrained_gpt2" \ --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": "Anish13/pretrained_gpt2", "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 "Anish13/pretrained_gpt2" \ --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": "Anish13/pretrained_gpt2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Anish13/pretrained_gpt2 with Docker Model Runner:
docker model run hf.co/Anish13/pretrained_gpt2
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Anish13/pretrained_gpt2")
model = AutoModelForCausalLM.from_pretrained("Anish13/pretrained_gpt2")Quick Links
pretrained_gpt2
This model is a fine-tuned version of gpt2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 5.4616
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: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 6.48 | 1.17 | 500 | 6.1099 |
| 5.5792 | 2.35 | 1000 | 5.7804 |
| 5.1247 | 3.52 | 1500 | 5.6199 |
| 4.7774 | 4.69 | 2000 | 5.5176 |
| 4.4701 | 5.87 | 2500 | 5.4713 |
| 4.1836 | 7.04 | 3000 | 5.4591 |
| 3.9183 | 8.22 | 3500 | 5.4620 |
| 3.7163 | 9.39 | 4000 | 5.4616 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1
- Datasets 2.15.0
- Tokenizers 0.15.0
- Downloads last month
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Model tree for Anish13/pretrained_gpt2
Base model
openai-community/gpt2
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Anish13/pretrained_gpt2")