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Model: eulogik/Bharat-Tiny-LLM-v2 Source: Original Platform
2026-07-25 18:04:11 +08:00
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+---
+tags:
+- hindi
+- devanagari
+- brahmi
+- qwen2.5
+- transformers
+- safetensors
+- edge-ai
+- india
+- llm
+- pytorch
+license: apache-2.0
+language:
+- hi
+- en
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+<p align="center">
+  <img src="https://img.shields.io/badge/Model_Size-1.5B-blue?style=flat-square">
+  <img src="https://img.shields.io/badge/Format-PyTorch_Safetensors-brightgreen?style=flat-square">
+  <img src="https://img.shields.io/badge/Hindi_Compression-33.8%25-orange?style=flat-square">
+  <img src="https://img.shields.io/badge/License-Apache_2.0-success?style=flat-square">
+  <img src="https://img.shields.io/badge/Made_by-eulogik-purple?style=flat-square">
+</p>
+
+<h1 align="center">🇮🇳 Bharat-Tiny-LLM v2 (PyTorch / Transformers Base)</h1>
+<p align="center"><em>1.5B Parameter Open-Weights Indic LLM featuring Brahmi Token Injection for 33.8% token compression and +36% faster Hindi inference.</em></p>
+
+---
+
+![Bharat-Tiny-LLM v2 Technical Infographic](bharat_tiny_llm_v2_tech_infographic.jpg)
+
+---
+
+## 🎯 Model Overview
+
+**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.
+
+---
+
+## 🎨 Architectural Pipeline & Training Methodology
+
+![Brahmi Token Injection Architecture](bharat_tiny_llm_v2_pipeline_diagram.jpg)
+
+### Interactive Training Pipeline Flowchart
+
+```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)"]
+```
+
+---
+
+## ✨ Key Benchmarks & Technical Advantages
+
+| 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** |
+
+---
+
+## 🚀 Quick Start with PyTorch & Transformers
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "eulogik/Bharat-Tiny-LLM-v2"
+
+# Load Tokenizer & Model Weights
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+# Generate Hindi Text
+prompt = "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))
+```
+
+---
+
+## 📊 Token Compression Benchmarks
+
+| 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** | **36% Savings** |
+| `"bhai aaj ka weather kaisa hai?"` | 12 tokens | **8 tokens** | **33% Savings** |
+
+---
+
+## 🔗 Model Family Repositories
+
+- **Apple Silicon MLX Quantized (880MB)**: [`eulogik/Bharat-Tiny-LLM-v2-MLX`](https://huggingface.co/eulogik/Bharat-Tiny-LLM-v2-MLX)
+- **GGUF / llama.cpp**: [`eulogik/Bharat-Tiny-LLM-GGUF`](https://huggingface.co/eulogik/Bharat-Tiny-LLM-GGUF)
+
+---
+
+## 📜 License & Citation
+
+Licensed 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}}
+}
+```