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| title: MGZon Chatbot | |
| emoji: "🤖" | |
| colorFrom: "blue" | |
| colorTo: "green" | |
| sdk: docker | |
| app_file: main.py | |
| pinned: false | |
| # MGZON-AI | |
| A versatile chatbot powered by MGZON/Veltrix for MGZon queries. Supports code generation, analysis, review, web search, and MGZon-specific queries. Licensed under Apache 2.0. | |
| --- | |
| library_name: transformers | |
| license: apache-2.0 | |
| 🌐 **Live Demo** | |
| [Live Demo](https://huggingface.co/spaces/MGZON/mgzon-app) | |
| base_model: MGZON/Veltrix | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: mgzon-flan-t5-base | |
| results: [] | |
| --- | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # MGZON/Veltrix | |
| This model is a fine-tuned version of [MGZON/Veltrix](https://huggingface.co/MGZON/Veltrix) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: nan | |
| ## Features | |
| - **Text Queries**: Ask anything and get detailed responses. | |
| - **Audio Input/Output**: Record audio directly or convert text to speech. | |
| - **Image Analysis**: Capture images from webcam or upload for analysis. | |
| - **Web Search**: Enable DeepSearch for real-time web context. | |
| - **API Support**: Use endpoints like `/api/chat`, `/api/audio-transcription`, `/api/text-to-speech`, `/api/image-analysis`. | |
| ## Setup | |
| 1. Add `HF_TOKEN` and `BACKUP_HF_TOKEN` as Secrets in Space settings. | |
| 2. Add `GOOGLE_API_KEY` and `GOOGLE_CSE_ID` for web search (optional). | |
| 3. Set `PORT=7860`, `QUEUE_SIZE=80`, `CONCURRENCY_LIMIT=20` as Variables. | |
| 4. Ensure `requirements.txt` and `Dockerfile` are configured correctly. | |
| ## Usage | |
| Access the app at `/gradio` or use API endpoints. Examples: | |
| - **Text**: "Explain AI history." | |
| - **Audio**: Record audio for transcription. | |
| - **Image**: Capture or upload an image for analysis. | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 3e-05 | |
| - train_batch_size: 1 | |
| - eval_batch_size: 1 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 2 | |
| - total_train_batch_size: 2 | |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - num_epochs: 5 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 0.2456 | 1.0 | 1488 | nan | | |
| | 0.0888 | 2.0 | 2976 | nan | | |
| | 15.9533 | 3.0 | 4464 | nan | | |
| | 0.1136 | 4.0 | 5952 | nan | | |
| | 0.0626 | 5.0 | 7440 | nan | | |
| ### Framework versions | |
| - Transformers 4.55.2 | |
| - Pytorch 2.8.0+cu126 | |
| - Datasets 4.0.0 | |
| - Tokenizers 0.21.4 | |