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Param-1

BharatGen introduces Param-1, a bilingual language model pretrained from scratch on English and Hindi. With 2.9 billion parameters, it serves as a powerful foundational model for text completion.

Param-1 outperforms leading models like LLaMA-3.2B, Gemma-2B, Granite-2B, and Granite-3B on various standard benchmarks.

This early release is equipped with inference support via NVIDIA NeMo.

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🚀 Model Inference

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load tokenizer and model
model_name = "bharatgenai/Param-1"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=False)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.bfloat32,
    device_map="auto"
)

prompt = "Your prompt here."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

# --- Generate output ---
with torch.no_grad():
    output = model.generate(
        **inputs,
        max_new_tokens=300,
        do_sample=True,
        top_k=50,
        top_p=0.95,
        temperature=0.6,
        eos_token_id=tokenizer.eos_token_id,
        use_cache=False
    )

generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print("Generated Text:\n", generated_text)

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📊 Benchmarks

Task	Param-1 (PT)
ARC Challenge	53.6 (few)
ARC Easy	74.2 (few)
HellaSwag	73.8 (few)
HellaSwag Hi	43.1 (few)
MMLU En	46.2 (few)
MMLU Hi	34.6 (few)
TriviaQA	42.8
TruthfulQA - Gen (BLEU)	37.3
TruthfulQA - MC1 Acc	28.4
TruthfulQA - MC2 Acc	42.9
PIQA	79.2
SuperGLUE - WiC	50.6
SuperGLUE - WSC	52.9
SuperGLUE - boolq	72.6
SuperGLUE - rte	66.8

Notes:

• PT: Pre-Trained
• en-hi: English-Hindi
• Pre-trained on 5 Trillion tokens

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🧠 Model Architecture

• Hidden size: 2048
• Intermediate size: 7168
• Number of attention heads: 16
• Number of hidden layers: 32
• Number of key-value heads: 8
• Maximum position embeddings: 2048
• Activation function: SiLU
• Positional embeddings: Rotary (RoPE) with rope_theta=10000.0
• Attention: Grouped-query attention
• Precision: bf16-mixed

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🏗️ Training Details

• Training Infrastructure: Yotta’s Shakti Cloud
• Hardware: NVIDIA H100 – 512 GPUs
• Framework: NVIDIA NeMo

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