Hulk733/Empirix

#!/usr/bin/env bash

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One-step scaffold + build + push script for EmpiriX model repo

Usage: bash deploy_empirix.sh [--push]

Optional ENV vars: AWS_ACCOUNT_ID, AWS_REGION (defaults to us-east-1)

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set -euo pipefail

Configurable vars

REPO_DIR="EmpiriX" MODEL_NAME="empirix" AWS_REGION="${AWS_REGION:-us-east-1}" AWS_ACCOUNT_ID="${AWS_ACCOUNT_ID:?Set AWS_ACCOUNT_ID env var for ECR push}"

echo "🛠️ Scaffolding repository structure..." mkdir -p "${REPO_DIR}/tokenizer"

1. config.json

cat > "${REPO_DIR}/config.json" << 'EOF' { "architectures": ["EmpiriXModel"], "hidden_size": 512, "num_attention_heads": 8, "num_hidden_layers": 6, "initializer_range": 0.02, "vocab_size": 30000, "pad_token_id": 0 } EOF

2. tokenizer files

cat > "${REPO_DIR}/tokenizer/vocab.txt" << 'EOF' EOF

cat > "${REPO_DIR}/tokenizer/tokenizer_config.json" << 'EOF' { "do_lower_case": true, "unk_token": "[UNK]", "pad_token": "[PAD]" } EOF

3. modeling.py

cat > "${REPO_DIR}/modeling.py" << 'EOF' from transformers import PreTrainedModel, PretrainedConfig

class EmpiriXConfig(PretrainedConfig): model_type = "empirix"

class EmpiriXModel(PreTrainedModel): config_class = EmpiriXConfig def init(self, config): super().init(config) # define your layers here

def forward(self, input_ids, attention_mask=None):
    # implement forward logic
    return self._forward_impl(input_ids, attention_mask)

EOF

4. configuration.py

cat > "${REPO_DIR}/configuration.py" << 'EOF' from transformers import PretrainedConfig

class EmpiriXConfig(PretrainedConfig): model_type = "empirix" EOF

5. training.py

cat > "${REPO_DIR}/training.py" << 'EOF' from transformers import Trainer, TrainingArguments, AutoTokenizer from modeling import EmpiriXModel, EmpiriXConfig

def main(): config = EmpiriXConfig() model = EmpiriXModel(config) tokenizer = AutoTokenizer.from_pretrained("tokenizer", use_fast=True) args = TrainingArguments( output_dir="./results", num_train_epochs=3, per_device_train_batch_size=8, save_steps=500, save_total_limit=2 ) trainer = Trainer( model=model, args=args, train_dataset=[], # replace with your Dataset eval_dataset=[], # replace with your Dataset tokenizer=tokenizer ) trainer.train()

if name == "main": main() EOF

6. inference.py

cat > "${REPO_DIR}/inference.py" << 'EOF' from fastapi import FastAPI from pydantic import BaseModel from transformers import EmpiriXConfig, EmpiriXModel, AutoTokenizer

app = FastAPI() config = EmpiriXConfig.from_json_file("config.json") model = EmpiriXModel(config) tokenizer = AutoTokenizer.from_pretrained("tokenizer", use_fast=True)

class Request(BaseModel): input: str

@app.post("/infer") def infer(req: Request): inputs = tokenizer(req.input, return_tensors="pt") outputs = model(**inputs) return {"logits": outputs.logits.tolist()} EOF

7. requirements.txt

cat > "${REPO_DIR}/requirements.txt" << 'EOF' transformers>=4.0.0 torch>=2.0.0 fastapi>=0.85.0 uvicorn>=0.20.0 safetensors EOF

8. Dockerfile

cat > "${REPO_DIR}/Dockerfile" << 'EOF' FROM python:3.10-slim WORKDIR /app COPY requirements.txt . RUN pip install --no-cache-dir -r requirements.txt COPY . . EXPOSE 8080 CMD ["uvicorn", "inference:app", "--host", "0.0.0.0", "--port", "8080"] EOF

9. README.md & model_card.md

cat > "${REPO_DIR}/README.md" << 'EOF'

EmpiriX

A Proto-AGI-class on-device AI model.

Quick Start

1. Clone & scaffold

   git clone <YOUR_HF_URL> && cd ${REPO_DIR}
   bash deploy_empirix.sh