Instructions to use epsil/bhagvad_gita with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use epsil/bhagvad_gita with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="epsil/bhagvad_gita")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("epsil/bhagvad_gita") model = AutoModelForCausalLM.from_pretrained("epsil/bhagvad_gita") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use epsil/bhagvad_gita with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "epsil/bhagvad_gita" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "epsil/bhagvad_gita", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/epsil/bhagvad_gita
- SGLang
How to use epsil/bhagvad_gita 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 "epsil/bhagvad_gita" \ --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": "epsil/bhagvad_gita", "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 "epsil/bhagvad_gita" \ --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": "epsil/bhagvad_gita", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use epsil/bhagvad_gita with Docker Model Runner:
docker model run hf.co/epsil/bhagvad_gita
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
This is fine-tuned model on Bhagvad Gita and creates text based on prompts. Example of usage:
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("epsil/bhagvad_gita")
model = AutoModelForCausalLM.from_pretrained("epsil/bhagvad_gita")
Input
from transformers import pipeline
pipeline = pipeline('text-generation',model=model, tokenizer=tokenizer)
result = samples('Krishna show me the right path')[0]['generated_text']
print(result)
Output
Krishna show me the right path, and I also to remember the lessons, and to remember them right.
Sama! in His Day, and by Thy own Eternal Grace.
A man like that who shall come to us
Created by Saurabh Mishra
Made with ♥ in India
- Downloads last month
- 9