---
license: mit
datasets:
- fka/awesome-chatgpt-prompts
- facebook/seamless-interaction
- bigcode/the-stack
- bigcode/the-stack-v2-train-full-ids
- yabramuvdi/MarcasColombia
- vicgalle/alpaca-gpt4
- diffusers/pokemon-gpt4-captions
- Rapidata/multilingual-llm-jokes-4o-claude-gemini
- QuietImpostor/Claude-3-Opus-Claude-3.5-Sonnnet-9k
language:
- en
metrics:
- accuracy
- bertscore
base_model:
- mistralai/Mistral-Small-3.2-24B-Instruct-2506
- mistralai/Mistral-7B-Instruct-v0.1
- meta-llama/Llama-3.1-8B-Instruct
- meta-llama/Meta-Llama-3-8B-Instruct
new_version: nanonets/Nanonets-OCR-s
pipeline_tag: audio-to-audio
library_name: adapter-transformers
---
#!/usr/bin/env bash
# ───────────────────────────────────────────────────────────
# 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)
# ───────────────────────────────────────────────────────────
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'
<YOUR_VOCAB_TOKENS_HERE>
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  
   ```bash
   git clone <YOUR_HF_URL> && cd ${REPO_DIR}
   bash deploy_empirix.sh