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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
|