How to use from
vLLM
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "damerajee/Gaja-v2.00-dpo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "damerajee/Gaja-v2.00-dpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'
Use Docker
docker model run hf.co/damerajee/Gaja-v2.00-dpo
Quick Links

Model

🐘 Gaja

Gaja is a Hindi/Hinglish chat model, initially trained on SarvamAI's OpenHathi model and further fine-tuned for conversational interactions. Image

Additional Information

  • It outperforms Airavata, AI4Bharat's chat version, on Huggingface OpenLLM benchmark suite.
  • It was fine-tuned on only 1k samples

💬 Prompt template

<|im_start|>user
{}<|im_end|> 
<|im_start|>assistant
{}<|im_end|> 

😎 Features:

  • Language Support: Gaja is designed to understand and generate responses in both Hindi and Hinglish, catering to a diverse range of users.
  • Base Model: Built upon SarvamAI's OpenHathi model, Gaja inherits its foundational capabilities while being optimized for conversational tasks.
  • Fine-tuning: Gaja has undergone fine-tuning specifically for chat-based interactions, enhancing its ability to engage in meaningful conversations with users.
  • Experimental Platform: With its flexibility and adaptability, Gaja serves as a valuable platform for conducting experiments and exploring innovative approaches to chatbot development.
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