Text Generation
Transformers
PyTorch
TensorBoard
gpt2
Generated from Trainer
text-generation-inference
Instructions to use aashay96/indic-gpt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aashay96/indic-gpt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="aashay96/indic-gpt")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("aashay96/indic-gpt") model = AutoModelForCausalLM.from_pretrained("aashay96/indic-gpt") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use aashay96/indic-gpt with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "aashay96/indic-gpt" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "aashay96/indic-gpt", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/aashay96/indic-gpt
- SGLang
How to use aashay96/indic-gpt 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 "aashay96/indic-gpt" \ --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": "aashay96/indic-gpt", "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 "aashay96/indic-gpt" \ --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": "aashay96/indic-gpt", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use aashay96/indic-gpt with Docker Model Runner:
docker model run hf.co/aashay96/indic-gpt
indic-gpt
This model is a fine-tuned version of gpt2 on an Indian Language(https://ai4bharat.iitm.ac.in/corpora) dataset. Sample Dataset is present on https://huggingface.co/datasets/aashay96/indic-gpt. It achieves the following results on the evaluation set:
- Loss: 1.9482
Model description
Model is trained on multiple Indian Languages - Assamese, bengali, gujarati, Kannada, Malayalam,telugu, tamil, odhiya and punjabi.
Intended uses & limitations
More information needed
Training and evaluation data
TBD - Evaluation on indic_glue
Training procedure
Check the notebook!
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.3653 | 0.3 | 500 | 2.2985 |
| 2.2079 | 0.61 | 1000 | 2.0401 |
| 2.0396 | 0.91 | 1500 | 1.9482 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
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