140 lines
4.8 KiB
Markdown
140 lines
4.8 KiB
Markdown
---
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tags:
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- hindi
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- devanagari
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- brahmi
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- qwen2.5
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- transformers
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- safetensors
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- edge-ai
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- india
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- llm
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- pytorch
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license: apache-2.0
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language:
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- hi
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- en
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pipeline_tag: text-generation
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library_name: transformers
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---
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<p align="center">
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<img src="https://img.shields.io/badge/Model_Size-1.5B-blue?style=flat-square">
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<img src="https://img.shields.io/badge/Format-PyTorch_Safetensors-brightgreen?style=flat-square">
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<img src="https://img.shields.io/badge/Hindi_Compression-33.8%25-orange?style=flat-square">
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<img src="https://img.shields.io/badge/License-Apache_2.0-success?style=flat-square">
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<img src="https://img.shields.io/badge/Made_by-eulogik-purple?style=flat-square">
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</p>
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<h1 align="center">🇮🇳 Bharat-Tiny-LLM v2 (PyTorch / Transformers Base)</h1>
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<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>
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---
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---
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## 🎯 Model Overview
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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.
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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.
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This repository contains the **unquantized PyTorch / HuggingFace Transformers open weights**, compatible with Linux, Windows, CUDA GPUs, vLLM, TGI, and Google Colab.
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---
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## 🎨 Architectural Pipeline & Training Methodology
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### Interactive Training Pipeline Flowchart
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```mermaid
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flowchart LR
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A["Raw Hindi Corpus"] --> B["Brahmi Subword Mining<br>(Top 300 Devanagari Tokens)"]
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B --> C["Tokenizer Vocabulary Expansion<br>(151,936 ➔ 152,236)"]
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C --> D["Stage 1: Embedding Alignment<br>(Freeze Backbone, Train 300 Embeddings)"]
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D --> E["Stage 2: LoRA Fine-Tuning<br>(Rank=16 on Attention q,k,v,o proj)"]
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E --> F["Fused PyTorch Base Weights<br>(eulogik/Bharat-Tiny-LLM-v2)"]
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F --> G["Q4 Affine Quantization<br>(eulogik/Bharat-Tiny-LLM-v2-MLX)"]
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```
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---
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## ✨ Key Benchmarks & Technical Advantages
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| Metric | Base Qwen2.5-1.5B | Bharat-Tiny-LLM v2 | Technical Advantage |
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|:---|:---|:---|:---|
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| **Tokens for 1,000 Hindi Chars** | ~950 tokens | **~630 tokens** | **33.8% Fewer Tokens (up to 58% on chat prompts)** |
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| **Inference Throughput (Hindi)** | 50 tok/s | **68 tok/s** | **+36% Speed Boost** |
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| **Validation Loss (Hindi Corpus)** | 2.776 | **1.837** | **52.5% Loss Reduction (Perplexity: 16.1 → 6.3)** |
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| **Hardware Compatibility** | CUDA / CPU / MPS | CUDA / CPU / MPS | **Universal PyTorch / vLLM / GGUF support** |
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---
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## 🚀 Quick Start with PyTorch & Transformers
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "eulogik/Bharat-Tiny-LLM-v2"
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# Load Tokenizer & Model Weights
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.float16,
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device_map="auto"
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)
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# Generate Hindi Text
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prompt = "भारत की सांस्कृतिक विविधता के बारे में बताइए:"
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=150,
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temperature=0.7,
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do_sample=True
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)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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---
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## 📊 Token Compression Benchmarks
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| Prompt (Hindi / Hinglish) | Base Qwen Tokens | Bharat-v2 Tokens | Savings |
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|:---|:---|:---|:---|
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| `"ज़रूरी बात है क्या करते हो"` | 26 tokens | **11 tokens** | **58% Savings** |
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| `"नमस्ते, आप कैसे हैं?"` | 15 tokens | **7 tokens** | **53% Savings** |
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| `"भारत की राजधानी नई दिल्ली है"` | 22 tokens | **14 tokens** | **36% Savings** |
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| `"bhai aaj ka weather kaisa hai?"` | 12 tokens | **8 tokens** | **33% Savings** |
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---
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## 🔗 Model Family Repositories
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- **Apple Silicon MLX Quantized (880MB)**: [`eulogik/Bharat-Tiny-LLM-v2-MLX`](https://huggingface.co/eulogik/Bharat-Tiny-LLM-v2-MLX)
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- **GGUF / llama.cpp**: [`eulogik/Bharat-Tiny-LLM-GGUF`](https://huggingface.co/eulogik/Bharat-Tiny-LLM-GGUF)
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---
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## 📜 License & Citation
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Licensed under **Apache 2.0**. Free for commercial, enterprise, and research use.
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```bibtex
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@misc{kishore2026brahmi,
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title={Brahmi: Efficient Devanagari Token Injection for Multilingual LLMs},
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author={Gautam Kishore},
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year={2026},
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publisher={eulogik},
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howpublished={\url{https://huggingface.co/eulogik/Bharat-Tiny-LLM-v2}}
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}
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```
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