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