bharatgenai / Param-1-7B

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Param-1-7B-MoE

Param-1-7B-MoE is a multilingual large language model developed under the Param-1 family as part of BharatGen – A Suite of Generative AI Technologies for India . With 7 billion parameters and a Mixture of Experts (MoE) architecture, the model is designed to better understand and generate text across English, Hindi, and 14 additional Indian languages .

The model is pretrained from scratch with a strong focus on linguistic diversity, cultural context, and large-scale multilingual representation relevant to India.

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Key Highlights

• 7B parameter Mixture of Experts (MoE) language model
• Multilingual : English, Hindi + 14 Indian languages
• Trained on 4 trillion tokens
• Uses 64 specialized experts , dynamically activated per token
• Supports long-context understanding (up to 4096 tokens)
• Designed as a pretrained (PT) base model for downstream fine-tuning

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Supported Languages

In addition to English and Hindi , the model has been trained on data from the following 14 Indian languages :

• Assamese
• Bengali
• Gujarati
• Kannada
• Maithili
• Malayalam
• Marathi
• Nepali
• Oriya
• Punjabi
• Sanskrit
• Sindhi
• Tamil
• Telugu

This broad language coverage enables better performance in region-specific applications and improves inclusivity across India’s linguistic landscape.

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Model Inference

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "bharatgenai/Param-1-7B-MoE"

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.float32,
    device_map="auto"
)

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

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
    )

print(tokenizer.decode(output[0], skip_special_tokens=True))

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Model Architecture

• Architecture: Transformer (Decoder-only) with Mixture of Experts (MoE)
• Number of parameters: ~7B
• Active parameters: 1.04B
• Number of experts: 64
• Expert routing: Token-level sparse activation, Top-K = 8 experts
• Maximum sequence length: 4096
• Positional embeddings: Rotary (RoPE)
• Attention mechanism: Optimized attention with modern activation techniques
• Precision: bf16-mixed

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Training Data

The model is pretrained on a large-scale multilingual corpus totaling 4 trillion tokens .

Dataset Composition

PT 1 : Pre-Training Phase 1

• English + Hindi : ~`500B` tokens
• Code + Math ~`500B` tokens
• 14 Indian languages : ~`1.0T` tokens

PT-2 : Pre-Training Phase 2

Note: All figures represent net token counts . Multilingual, SFT, RL, and replay data are re-expressions of the same underlying knowledge and are not additive .

• General Knowledge : ~`470B` tokens

• Technical Knowledge (Research + STEM + Philosophy + India-focused research) : ~`270B` tokens

• Education, Exams, Domain Specialisation, Math, Benchmarks & Code : ~`330B` tokens

• Conversational Knowledge (Forums, Q&A) : ~`10B` tokens

• Indic Multilingual Expansion (16 languages; synthetic + OCR + translation + personas) : ~`920B` tokens

• Alignment & Stabilization (within budget) :

  • SFT-style : ~`45B` tokens
  • RL / Preference / Safety : ~`4.9B` tokens
  • PT-1 replay (low-resource stabilization) : ~`95B` tokens
  • LLM Self-Identity & BharatGen knowledge : ~`0.1B` tokens

Total PT-2 Tokens: ~`2.0T`

The data mixture was curated and balanced using CLIMB (Clustering-based Iterative Data Mixture Bootstrapping) , an advanced data filtering and mixing technique from NVIDIA. This ensures high-quality training signals and fair representation across languages.

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Training Details

• Training framework: NVIDIA NeMo
• Training infrastructure: Yotta Shakti Cloud
• Hardware: NVIDIA H100 GPUs

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Limitations

• This is a pretrained base model and may require fine-tuning for instruction-following or chat-based applications.
• Model outputs may reflect biases present in large-scale multilingual web data.
• Performance may vary across low-resource domains and specialized tasks.

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License

This model is released under the BharatGen non-commercial license .

Please refer to the LICENSE file for detailed terms and conditions.

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