Text Generation
Transformers
PyTorch
gpt2
Generated from Trainer
Eval Results (legacy)
text-generation-inference
Instructions to use taufeeque/tiny-gpt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use taufeeque/tiny-gpt2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="taufeeque/tiny-gpt2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("taufeeque/tiny-gpt2") model = AutoModelForCausalLM.from_pretrained("taufeeque/tiny-gpt2") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use taufeeque/tiny-gpt2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "taufeeque/tiny-gpt2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "taufeeque/tiny-gpt2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/taufeeque/tiny-gpt2
- SGLang
How to use taufeeque/tiny-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 "taufeeque/tiny-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": "taufeeque/tiny-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 "taufeeque/tiny-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": "taufeeque/tiny-gpt2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use taufeeque/tiny-gpt2 with Docker Model Runner:
docker model run hf.co/taufeeque/tiny-gpt2
metadata
tags:
- generated_from_trainer
datasets:
- wikitext
metrics:
- accuracy
model-index:
- name: output_tiny
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: wikitext wikitext-103-v1
type: wikitext
args: wikitext-103-v1
metrics:
- name: Accuracy
type: accuracy
value: 0.2132901596611274
output_tiny
This model is a fine-tuned version of gpt2_tiny_random on the wikitext wikitext-103-v1 dataset. It achieves the following results on the evaluation set:
- Loss: 5.3359
- Accuracy: 0.2133
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: 0.0001
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 50000
Training results
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.13.2