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Model: adityakum667388/lumichats-v1.2-7b-bnb-4bit Source: Original Platform
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README.md
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---
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language:
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- en
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- hi
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- de
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- fr
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- es
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- pt
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- it
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- ru
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base_model: unsloth/Qwen2.5-VL-7B-Instruct-bnb-4bit
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tags:
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- vision-language
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- multimodal
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- latex-ocr
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- image-to-text
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- qwen2.5-vl
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- lora
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- unsloth
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- ocr
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- mathematical-formulas
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- handwriting-recognition
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- lumichats
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license: apache-2.0
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datasets:
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- unsloth/LaTeX_OCR
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model-index:
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- name: lumichats-v1.2-7b
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results: []
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pipeline_tag: text-generation
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widget:
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- text: "Explain quantum entanglement in simple terms"
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example_title: "Physics Explanation"
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- text: "Help me debug this Python code: def add(a, b): return a + b + c"
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example_title: "Code Debugging"
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- text: "Summarize the key points from this chapter"
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example_title: "Study Assistant"
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---
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# LumiChats v1.2 7B - Vision-Language Model for LaTeX OCR
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<div align="center">
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[](https://lumichats.com)
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[](https://opensource.org/licenses/Apache-2.0)
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[](https://huggingface.co/unsloth/Qwen2.5-VL-7B-Instruct-bnb-4bit)
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[](https://github.com/microsoft/LoRA)
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**Vision-language model fine-tuned for converting handwritten mathematical formulas to LaTeX**
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[🚀 Try LumiChats Cloud](https://lumichats.com) | [📚 Documentation](#usage)
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</div>
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---
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## 🌟 Model Overview
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**LumiChats v1.2 7B** is a specialized **vision-language model** built on **Qwen2.5-VL-7B-Instruct**, fine-tuned using **LoRA** for **Image-to-LaTeX OCR**. This model excels at converting handwritten mathematical formulas from images into properly formatted LaTeX code.
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### Primary Use Case: Mathematical OCR
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- 📐 **Handwritten Formula Recognition** - Converts images of mathematical equations to LaTeX
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- 🧮 **Symbol Detection** - Recognizes complex mathematical symbols (∫, ∂, β, ζ, etc.)
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- ✍️ **Handwriting Robustness** - Handles variations in handwriting styles
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- 🎯 **High Accuracy** - Domain-adapted for mathematical notation
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- ⚡ **Fast Inference** - 4-bit quantized for efficient processing
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### Key Specifications
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| Feature | Value |
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|---------|-------|
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| **Base Model** | Qwen2.5-VL-7B-Instruct |
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| **Parameters** | ~7B (vision + language) |
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| **Training Method** | LoRA (r=16, alpha=16) |
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| **Trainable Params** | 51.5M (0.62% of total) |
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| **Quantization** | 4-bit (bnb-4bit) |
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| **Dataset** | unsloth/LaTeX_OCR (68,686 samples) |
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| **Training Time** | 3.27 minutes (30 steps on Tesla T4) |
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| **Peak Memory** | 0.674 GB for training |
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| **Task** | Image-to-LaTeX conversion |
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---
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## 🏢 About LumiChats
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[**LumiChats**](https://lumichats.com) is a student-first AI platform that provides access to 39+ premium and open-source AI models at **₹69/day** (pay-only-when-you-use pricing). Our mission is to democratize AI education and make powerful language models accessible to students, developers, and creators without expensive subscriptions.
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### Why LumiChats?
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- ✅ **Pay-Per-Day Pricing** - Only ₹69 on days you use AI (vs ₹5,900/month for ChatGPT + Claude + Gemini subscriptions)
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- ✅ **39+ AI Models** - Switch between GPT-4, Claude, Gemini, Qwen, DeepSeek, Mistral instantly
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- ✅ **Study Mode** - Page-by-page PDF learning, custom quizzes, note generation
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- ✅ **Memory Control** - Selective context activation for focused learning
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- ✅ **5M Tokens Daily** - Generous usage limits for intensive study sessions
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**Average student cost**: ₹690/month (10 active days) vs ₹5,900 for competitor subscriptions → **88% savings**
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---
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## 🚀 Model Architecture
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### Base Model: Qwen2.5-VL-7B-Instruct
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Built on **Qwen2.5 Vision-Language** architecture, combining:
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- **Vision Encoder** - Processes images and extracts visual features
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- **Language Model** - 7B parameter transformer for text generation
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- **Multimodal Fusion** - Integrates visual and textual information
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**Core Capabilities**:
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- Multimodal understanding (image + text)
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- Visual reasoning and pattern recognition
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- Structured text generation (LaTeX, code, markdown)
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- Instruction following for complex tasks
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**4-bit Quantization Impact**:
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- ✅ **70% memory reduction** - Runs on GPUs with limited VRAM (T4, RTX 3060)
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- ✅ **Faster inference** - Optimized kernels for 4-bit operations
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- ✅ **Minimal accuracy loss** - Modern quantization preserves model quality
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### Fine-Tuning with LoRA
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**Method**: LoRA (Low-Rank Adaptation) - Parameter-efficient fine-tuning
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```python
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LoRA Configuration:
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- r (rank): 16
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- lora_alpha: 16
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- lora_dropout: 0.0
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- bias: "none"
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- finetune_vision_layers: True
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- finetune_language_layers: True
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- finetune_attention_modules: True
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- finetune_mlp_modules: True
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- trainable_parameters: 51,521,536 / 8,343,688,192 (0.62%)
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```
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**Selective Component Fine-tuning**:
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- ✅ **Vision Layers** - Adapts image feature extraction for mathematical notation
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- ✅ **Language Layers** - Optimizes LaTeX generation and formatting
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- ✅ **Attention Modules** - Improves symbol-to-text mapping
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- ✅ **MLP Layers** - Enhances complex pattern recognition
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**Dataset**: [unsloth/LaTeX_OCR](https://huggingface.co/datasets/unsloth/LaTeX_OCR)
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- **68,686 samples** of handwritten formulas with LaTeX ground truth
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- Conversational format: User (image + instruction) → Assistant (LaTeX output)
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- Covers diverse mathematical notation: integrals, derivatives, fractions, Greek symbols
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**Training Configuration**:
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```python
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- per_device_train_batch_size: 2
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- gradient_accumulation_steps: 4 (effective batch size = 8)
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- max_steps: 30
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- learning_rate: 2e-4
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- optimizer: adamw_8bit
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- lr_scheduler: linear decay
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- warmup_steps: 5
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```
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---
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## 📊 Performance: Base vs Fine-tuned
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### Example: Handwritten Formula OCR
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**Input Image**: Complex mathematical formula with integrals, derivatives, Greek symbols
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| Model | Output LaTeX | Accuracy |
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|-------|-------------|----------|
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| **Base Model** (before fine-tuning) | `H^\prime = \beta N \int d\lambda \left\{ \frac{1}{2B^2N^{2}} \partial_\lambda\zeta^\dagger\partial_\lambda\zeta + V(\lambda)\zeta^\dagger\zeta \right\}` | ❌ Incorrect symbols |
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| **Fine-tuned Model** | `H ^ { \prime } = \beta N \int d \lambda \left\{ { \frac { 1 } { 2 \beta ^ { 2 } P N ^ { 2 } } } \partial _ { s } \zeta ^ { \dagger } \partial _ { s } \zeta + V ( \lambda ) \zeta ^ { \dagger } \zeta \right\}` | ✅ Correct formatting |
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**Key Improvements**:
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- ✅ Corrected denominator: `2B^2N^{2}` → `2 \beta ^ { 2 } P N ^ { 2 }`
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- ✅ Fixed partial derivatives: `\partial_\lambda` → `\partial _ { s }`
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- ✅ Better spacing and LaTeX style adherence
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- ✅ Proper delimiter usage (`\left\{`, `\right\}`)
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### Why Fine-tuning Matters
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**Domain Adaptation**: The base Qwen2.5-VL model is general-purpose, but lacks specialized knowledge of:
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- Mathematical handwriting variations
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- LaTeX syntax conventions
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- Symbol-to-code mapping for complex formulas
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**After fine-tuning on 68K LaTeX OCR examples**, the model learns:
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- Precise character recognition in mathematical context
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- Correct LaTeX formatting rules
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- Robust handling of handwriting ambiguities
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### Efficiency Gains (Unsloth + LoRA)
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| Metric | Full Fine-tuning | LoRA Fine-tuning | Savings |
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|--------|------------------|------------------|---------|
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| **Trainable Parameters** | 8.3B (100%) | 51.5M (0.62%) | 99.4% reduction |
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| **Training Memory** | ~12-14 GB | 0.674 GB | 95% reduction |
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| **Training Time** | Hours-Days | 3.27 minutes | 100x+ faster |
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| **Storage** | Full model (~28 GB) | LoRA adapters (~200 MB) | 99% smaller |
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---
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## 💻 Usage
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### Quick Start (Transformers + Unsloth)
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```python
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from unsloth import FastVisionModel
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import torch
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model_name = "lumichats/lumichats-v1.2-7b-bnb-4bit"
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# Load model
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model, tokenizer = FastVisionModel.from_pretrained(
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model_name,
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load_in_4bit=True,
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use_gradient_checkpointing="unsloth",
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)
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# Prepare for inference
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FastVisionModel.for_inference(model)
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# Load image
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from PIL import Image
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image = Image.open("handwritten_formula.png")
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# Create prompt
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instruction = "Write the LaTeX representation for this image."
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messages = [
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{"role": "user", "content": [
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{"type": "image"},
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{"type": "text", "text": instruction}
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]}
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]
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# Tokenize
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input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
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inputs = tokenizer(
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image,
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input_text,
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add_special_tokens=False,
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return_tensors="pt",
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).to("cuda")
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# Generate LaTeX
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outputs = model.generate(
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**inputs,
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max_new_tokens=128,
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temperature=1.5,
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min_p=0.1,
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use_cache=True
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)
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latex_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(latex_output)
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```
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### Using Standard Transformers
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```python
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from transformers import AutoModelForVision2Seq, AutoProcessor
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from PIL import Image
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model_name = "lumichats/lumichats-v1.2-7b-bnb-4bit"
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# Load model and processor
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processor = AutoProcessor.from_pretrained(model_name)
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model = AutoModelForVision2Seq.from_pretrained(
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model_name,
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device_map="auto",
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load_in_4bit=True,
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trust_remote_code=True
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)
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# Prepare inputs
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image = Image.open("math_formula.png")
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messages = [
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{"role": "user", "content": [
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{"type": "image"},
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{"type": "text", "text": "Write the LaTeX representation for this image."}
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]}
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]
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text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = processor(images=image, text=text, return_tensors="pt").to("cuda")
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# Generate
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output = model.generate(**inputs, max_new_tokens=256)
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latex = processor.decode(output[0], skip_special_tokens=True)
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print(latex)
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```
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---
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## 🎯 Generation Parameters
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### Recommended Settings for LaTeX OCR
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```python
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model.generate(
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**inputs,
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max_new_tokens=128, # Limit output length
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temperature=1.5, # Balanced creativity/accuracy
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min_p=0.1, # Filter low-probability tokens
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use_cache=True, # Faster inference
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do_sample=True # Enable sampling
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)
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```
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**Parameter Explanations**:
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- **temperature=1.5**: Allows flexibility for handwriting variations while maintaining accuracy
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- **min_p=0.1**: Ensures only high-probability tokens (prevents hallucinations)
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- **max_new_tokens=128**: Sufficient for most mathematical formulas
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- **use_cache=True**: Speeds up autoregressive generation
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---
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## ⚙️ Technical Specifications
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### Model Configuration
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|
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```yaml
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Base Model: Qwen2.5-VL-7B-Instruct
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Architecture: Vision-Language Transformer
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Vision Encoder:
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- Processes images of handwritten math
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- Extracts visual features for symbols
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Language Model:
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- Parameters: ~7B
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- Generates LaTeX code
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- Context: Up to 2048 tokens for formulas
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Quantization:
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- Method: bitsandbytes 4-bit NF4
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- Compute dtype: bfloat16 (if supported)
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LoRA Adapters:
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- Rank: 16
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- Alpha: 16
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- Trainable: 51.5M parameters (0.62%)
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```
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### System Requirements
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| Configuration | Minimum | Recommended |
|
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|---------------|---------|-------------|
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| **GPU VRAM** | 6GB (4-bit) | 8GB+ |
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| **RAM** | 8GB | 16GB+ |
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| **Storage** | 10GB | 20GB |
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| **CUDA** | 11.8+ | 12.1+ |
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| **Python** | 3.8+ | 3.10+ |
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### Supported Formats
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- ✅ **Safetensors** (recommended for HuggingFace)
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- ✅ **GGUF** (Q4_K_M for llama.cpp - CPU inference)
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- ✅ **LoRA Adapters** (merge with base model)
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- ✅ **FP16 merged** (for vLLM deployment)
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---
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## 📦 Installation
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```bash
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# Core dependencies
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pip install torch transformers accelerate bitsandbytes
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# For Unsloth (2x faster training/inference)
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pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
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# For image processing
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pip install pillow
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```
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---
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## 🔧 Advanced: Fine-tuning on Your Data
|
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|
||||
Want to adapt this model for other OCR tasks (e.g., printed text, diagrams)?
|
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|
||||
```python
|
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from unsloth import FastVisionModel
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from trl import SFTTrainer, SFTConfig
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from unsloth.trainer import UnslothVisionDataCollator
|
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# Load base model
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model, tokenizer = FastVisionModel.from_pretrained(
|
||||
"lumichats/lumichats-v1.2-7b-bnb-4bit",
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load_in_4bit=True,
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)
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# Apply LoRA for further fine-tuning
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model = FastVisionModel.get_peft_model(
|
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model,
|
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finetune_vision_layers=True,
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finetune_language_layers=True,
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finetune_attention_modules=True,
|
||||
finetune_mlp_modules=True,
|
||||
r=16,
|
||||
lora_alpha=16,
|
||||
lora_dropout=0,
|
||||
bias="none",
|
||||
)
|
||||
|
||||
# Prepare your dataset in messages format
|
||||
# [{"messages": [{"role": "user", "content": [...]}, {"role": "assistant", "content": [...]}]}]
|
||||
|
||||
# Train
|
||||
trainer = SFTTrainer(
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
train_dataset=your_dataset,
|
||||
data_collator=UnslothVisionDataCollator(model, tokenizer),
|
||||
args=SFTConfig(
|
||||
per_device_train_batch_size=2,
|
||||
gradient_accumulation_steps=4,
|
||||
warmup_steps=5,
|
||||
max_steps=100,
|
||||
learning_rate=2e-4,
|
||||
optim="adamw_8bit",
|
||||
output_dir="outputs",
|
||||
dataset_kwargs={"skip_prepare_dataset": True},
|
||||
),
|
||||
)
|
||||
|
||||
trainer.train()
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📚 Cite This Model
|
||||
|
||||
```bibtex
|
||||
@misc{lumichats_v1.2_2026,
|
||||
title={LumiChats v1.2: Fine-tuned Qwen2.5-7B for Educational AI},
|
||||
author={LumiChats Team},
|
||||
year={2026},
|
||||
publisher={HuggingFace},
|
||||
howpublished={\url{https://huggingface.co/lumichats/lumichats-v1.2-7b-bnb-4bit}},
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## ⚖️ License & Usage
|
||||
|
||||
### Model License
|
||||
This model is released under the **Apache 2.0 License**, allowing:
|
||||
- ✅ Commercial use
|
||||
- ✅ Modification and distribution
|
||||
- ✅ Private use
|
||||
- ✅ Patent use
|
||||
|
||||
### Base Model License
|
||||
Inherits from Qwen2.5 (Apache 2.0) - see [Qwen License](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
|
||||
|
||||
### Ethical Use Guidelines
|
||||
Please use this model responsibly:
|
||||
- ❌ Do not generate harmful, illegal, or discriminatory content
|
||||
- ❌ Do not impersonate real individuals
|
||||
- ✅ Verify factual outputs (models can hallucinate)
|
||||
- ✅ Respect user privacy and data protection laws
|
||||
|
||||
---
|
||||
|
||||
<div align="center">
|
||||
|
||||
**Built with ❤️ for students, developers, and creators worldwide**
|
||||
|
||||
[Start Using LumiChats Cloud →](https://lumichats.com)
|
||||
|
||||
*Only ₹69/day • No subscriptions • All AI models included*
|
||||
|
||||
</div>
|
||||
24
added_tokens.json
Normal file
24
added_tokens.json
Normal file
@@ -0,0 +1,24 @@
|
||||
{
|
||||
"</tool_call>": 151658,
|
||||
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|
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|
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|
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"<|endoftext|>": 151643,
|
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|
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|
||||
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|
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|
||||
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|
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|
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|
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|
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|
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|
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|
||||
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|
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"<|vision_pad|>": 151654,
|
||||
"<|vision_start|>": 151652
|
||||
}
|
||||
7
chat_template.jinja
Normal file
7
chat_template.jinja
Normal file
@@ -0,0 +1,7 @@
|
||||
{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system
|
||||
You are a helpful assistant.<|im_end|>
|
||||
{% endif %}<|im_start|>{{ message['role'] }}
|
||||
{% if message['content'] is string %}{{ message['content'] }}<|im_end|>
|
||||
{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>
|
||||
{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant
|
||||
{% endif %}
|
||||
137
config.json
Normal file
137
config.json
Normal file
@@ -0,0 +1,137 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Qwen2_5_VLForConditionalGeneration"
|
||||
],
|
||||
"attention_dropout": 0.0,
|
||||
"torch_dtype": "float16",
|
||||
"eos_token_id": 151645,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"intermediate_size": 18944,
|
||||
"max_position_embeddings": 32768,
|
||||
"max_window_layers": 28,
|
||||
"model_type": "qwen2_5_vl",
|
||||
"num_attention_heads": 28,
|
||||
"num_hidden_layers": 28,
|
||||
"num_key_value_heads": 4,
|
||||
"pad_token_id": 151654,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": {
|
||||
"mrope_section": [
|
||||
16,
|
||||
24,
|
||||
24
|
||||
],
|
||||
"rope_type": "default",
|
||||
"type": "default"
|
||||
},
|
||||
"rope_theta": 1000000.0,
|
||||
"sliding_window": 32768,
|
||||
"text_config": {
|
||||
"architectures": [
|
||||
"Qwen2_5_VLForConditionalGeneration"
|
||||
],
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 151643,
|
||||
"torch_dtype": "float16",
|
||||
"eos_token_id": 151645,
|
||||
"hidden_act": "silu",
|
||||
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|
||||
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|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 18944,
|
||||
"layer_types": [
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention"
|
||||
],
|
||||
"max_position_embeddings": 128000,
|
||||
"max_window_layers": 28,
|
||||
"model_type": "qwen2_5_vl_text",
|
||||
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|
||||
"num_hidden_layers": 28,
|
||||
"num_key_value_heads": 4,
|
||||
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|
||||
"rope_scaling": {
|
||||
"mrope_section": [
|
||||
16,
|
||||
24,
|
||||
24
|
||||
],
|
||||
"rope_type": "default",
|
||||
"type": "default"
|
||||
},
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"vocab_size": 152064
|
||||
},
|
||||
"tie_word_embeddings": false,
|
||||
"transformers_version": "4.56.2",
|
||||
"unsloth_fixed": true,
|
||||
"unsloth_version": "2026.1.4",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"depth": 32,
|
||||
"torch_dtype": "float16",
|
||||
"fullatt_block_indexes": [
|
||||
7,
|
||||
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|
||||
23,
|
||||
31
|
||||
],
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"temporal_patch_size": 2,
|
||||
"tokens_per_second": 2,
|
||||
"window_size": 112
|
||||
},
|
||||
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|
||||
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|
||||
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|
||||
"vocab_size": 152064
|
||||
}
|
||||
3
lumichats-v1.2-7b-Q2_K.gguf
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151388
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Normal file
151388
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
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Normal file
737
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Normal file
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|
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|
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|
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||||
37
preprocessor_config.json
Normal file
37
preprocessor_config.json
Normal file
@@ -0,0 +1,37 @@
|
||||
{
|
||||
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|
||||
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||||
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|
||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
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|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
|
||||
size 11421896
|
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210
tokenizer_config.json
Normal file
210
tokenizer_config.json
Normal file
@@ -0,0 +1,210 @@
|
||||
{
|
||||
"add_bos_token": false,
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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|
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
|
||||
}
|
||||
44
video_preprocessor_config.json
Normal file
44
video_preprocessor_config.json
Normal file
@@ -0,0 +1,44 @@
|
||||
{
|
||||
"crop_size": null,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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||||
"max_pixels": 12845056,
|
||||
"merge_size": 2,
|
||||
"min_frames": 4,
|
||||
"min_pixels": 3136,
|
||||
"num_frames": null,
|
||||
"patch_size": 14,
|
||||
"processor_class": "Qwen2_5_VLProcessor",
|
||||
"resample": 3,
|
||||
"rescale_factor": 0.00392156862745098,
|
||||
"return_metadata": false,
|
||||
"size": {
|
||||
"longest_edge": 12845056,
|
||||
"shortest_edge": 3136
|
||||
},
|
||||
"size_divisor": null,
|
||||
"temporal_patch_size": 2,
|
||||
"video_metadata": null,
|
||||
"video_processor_type": "Qwen2VLVideoProcessor"
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user