Instructions to use epsil/bhagvad_gita with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("epsil/bhagvad_gita")
model = AutoModelForMultimodalLM.from_pretrained("epsil/bhagvad_gita")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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: 5

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for epsil/bhagvad_gita

Quantizations

2 models