first commit

.dockerignore

@@ -0,0 +1,6 @@
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+*.git
+*.venv

.gitattributes

@@ -0,0 +1 @@
+model/*.safetensors filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -0,0 +1,10 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+COPY . .
+
+RUN pip install --no-cache-dir --upgrade pip
+RUN pip install --no-cache-dir -r requirements.txt
+
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8080"]

README.md

@@ -1,2 +1,73 @@
+<<<<<<< HEAD
 # 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
+
+## 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
+>>>>>>> 38501a7 (Initial commit with API and model)

app/main.py

@@ -0,0 +1,25 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+
+app = FastAPI(title="MGZON FLAN-T5 API")
+
+# تحميل النموذج من Hugging Face مباشرة
+MODEL_NAME = "MGZON/mgzon-flan-t5-base"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
+
+class RequestText(BaseModel):
+    text: str
+    max_length: int = 200
+
+@app.post("/generate/")
+async def generate(req: RequestText):
+    inputs = tokenizer(req.text, return_tensors="pt")
+    outputs = model.generate(**inputs, max_length=req.max_length)
+    return {"generated_text": tokenizer.decode(outputs[0], skip_special_tokens=True)}
+
+@app.get("/")
+async def root():
+    return {"message": "MGZON FLAN-T5 API is running"}

requirements.txt

@@ -0,0 +1,4 @@
+fastapi
+uvicorn[standard]
+transformers
+torch