Gaja-v1.00

Model

🐘 Gaja

Gaja is a Hindi/Hinglish chat model, initially trained on SarvamAI's OpenHathi model and further fine-tuned for conversational interactions. <img src="https://cdn-uploads.huggingface.co/production/uploads/6487239cca30096ea9f52115/sLgskU1h2Tih_bJo0ktYR.jpeg" width="600" alt="Image">

Inference

hey guys thanks to Bhabha AI, you guys can finally try my model

Additional Information

• It outperforms Airavata, AI4Bharat's chat version, on Huggingface OpenLLM benchmark suite.
• It was fine-tuned on only 1k samples
• The code for this can be found in The github code - Github

💬 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.

😤 Example Output

Eval

Hindi eval

English eval

🚀 Infernce(colab or kaggle notebooks)

Installing dependencies

!pip install -q peft bitsandbytes datasets accelerate

Load the model

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("damerajee/Gaja-v1.00")
model = AutoModelForCausalLM.from_pretrained("damerajee/Gaja-v1.00",load_in_4bit=True)

Try it out

messages = [
    {"role": "user", "content": "Why do poeple in India touch the feet of elders when they greet them?"},
]
inputs = tokenizer.apply_chat_template(
    messages,
    tokenize = True,
    add_generation_prompt = True, # Must add for generation
    return_tensors = "pt",
).to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(input_ids = inputs, streamer = text_streamer, max_new_tokens = 300, use_cache = True)

Open LLM Leaderboard Evaluation Results

Detailed results can be found here