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Bharat-Tiny-LLM v2 is an open-weights 1.5B parameter language model built on top of Qwen2.5-1.5B, optimized specifically for Hindi and Hinglish text generation.
By introducing Brahmi Token Injection — a technique that surgically injects 300 Devanagari subword tokens into the model's vocabulary — Bharat-Tiny-LLM v2 eliminates the severe "Token Tax" imposed by standard English-centric tokenizers on Indian scripts.
This repository contains the unquantized PyTorch / HuggingFace Transformers open weights, compatible with Linux, Windows, CUDA GPUs, vLLM, TGI, and Google Colab.
mermaid
flowchart LR A["Raw Hindi Corpus"] --> B["Brahmi Subword Mining<br>(Top 300 Devanagari Tokens)"] B --> C["Tokenizer Vocabulary Expansion<br>(151,936 ➔ 152,236)"] C --> D["Stage 1: Embedding Alignment<br>(Freeze Backbone, Train 300 Embeddings)"] D --> E["Stage 2: LoRA Fine-Tuning<br>(Rank=16 on Attention q,k,v,o proj)"] E --> F["Fused PyTorch Base Weights<br>(eulogik/Bharat-Tiny-LLM-v2)"] F --> G["Q4 Affine Quantization<br>(eulogik/Bharat-Tiny-LLM-v2-MLX)"]| Metric | Base Qwen2.5-1.5B | Bharat-Tiny-LLM v2 | Technical Advantage |
|---|---|---|---|
| Tokens for 1,000 Hindi Chars | ~950 tokens | ~630 tokens | 33.8% Fewer Tokens (up to 58% on chat prompts) |
| Inference Throughput (Hindi) | 50 tok/s | 68 tok/s | +36% Speed Boost |
| Validation Loss (Hindi Corpus) | 2.776 | 1.837 | 52.5% Loss Reduction (Perplexity: 16.1 → 6.3) |
| Hardware Compatibility | CUDA / CPU / MPS | CUDA / CPU / MPS | Universal PyTorch / vLLM / GGUF support |
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_name = "eulogik/Bharat-Tiny-LLM-v2" # Load Tokenizer & Model Weightstokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.float16, device_map="auto") # Generate Hindi Textprompt = "भारत की सांस्कृतिक विविधता के बारे में बताइए:"inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate( **inputs, max_new_tokens=150, temperature=0.7, do_sample=True) print(tokenizer.decode(outputs[0], skip_special_tokens=True))| Prompt (Hindi / Hinglish) | Base Qwen Tokens | Bharat-v2 Tokens | Savings |
|---|---|---|---|
"ज़रूरी बात है क्या करते हो" | 26 tokens | 11 tokens | 58% Savings |
"नमस्ते, आप कैसे हैं?" | 15 tokens | 7 tokens | 53% Savings |
"भारत की राजधानी नई दिल्ली है" | 22 tokens | 14 tokens |
eulogik/Bharat-Tiny-LLM-v2-MLXeulogik/Bharat-Tiny-LLM-GGUFLicensed under Apache 2.0. Free for commercial, enterprise, and research use.
bibtex
@misc{kishore2026brahmi, title={Brahmi: Efficient Devanagari Token Injection for Multilingual LLMs}, author={Gautam Kishore}, year={2026}, publisher={eulogik}, howpublished={\url{https://huggingface.co/eulogik/Bharat-Tiny-LLM-v2}}}flowchart LR A["Raw Hindi Corpus"] --> B["Brahmi Subword Mining<br>(Top 300 Devanagari Tokens)"] B --> C["Tokenizer Vocabulary Expansion<br>(151,936 ➔ 152,236)"] C --> D["Stage 1: Embedding Alignment<br>(Freeze Backbone, Train 300 Embeddings)"] D --> E["Stage 2: LoRA Fine-Tuning<br>(Rank=16 on Attention q,k,v,o proj)"] E --> F["Fused PyTorch Base Weights<br>(eulogik/Bharat-Tiny-LLM-v2)"] F --> G["Q4 Affine Quantization<br>(eulogik/Bharat-Tiny-LLM-v2-MLX)"]| 36% Savings |
"bhai aaj ka weather kaisa hai?" | 12 tokens | 8 tokens | 33% Savings |