prepare_model.py

# prepare_model.py
import os
import json
import shutil
from pathlib import Path

def create_minimal_model_structure(model_path="."):
    """
    Create minimal required files for Hugging Face model upload
    """
    
    # Create directories if they don't exist
    os.makedirs(model_path, exist_ok=True)
    
    # 1. Check for model files
    model_files = list(Path(model_path).glob("*.safetensors")) + \
                  list(Path(model_path).glob("*.bin")) + \
                  list(Path(model_path).glob("pytorch_model*.bin"))
    
    if not model_files:
        print("⚠️  Warning: No model weight files found!")
        print("   Expected: *.safetensors, *.bin, or pytorch_model*.bin")
    
    # 2. Create config.json if missing
    config_path = Path(model_path) / "config.json"
    if not config_path.exists():
        print("📝 Creating minimal config.json...")
        config = {
            "_name_or_path": "abdelac/Mistral_Test",
            "architectures": ["MistralForCausalLM"],  # Adjust based on your model
            "model_type": "mistral",
            "torch_dtype": "float16",
            "transformers_version": "4.35.0"
        }
        with open(config_path, "w") as f:
            json.dump(config, f, indent=2)
    
    # 3. Create tokenizer files if missing
    tokenizer_config_path = Path(model_path) / "tokenizer_config.json"
    if not tokenizer_config_path.exists():
        print("📝 Creating tokenizer_config.json...")
        tokenizer_config = {
            "bos_token": "<s>",
            "eos_token": "</s>",
            "pad_token": "</s>",
            "unk_token": "<unk>",
            "model_max_length": 32768,
            "clean_up_tokenization_spaces": False
        }
        with open(tokenizer_config_path, "w") as f:
            json.dump(tokenizer_config, f, indent=2)
    
    # 4. Create special_tokens_map.json
    special_tokens_path = Path(model_path) / "special_tokens_map.json"
    if not special_tokens_path.exists():
        print("📝 Creating special_tokens_map.json...")
        special_tokens = {
            "bos_token": "<s>",
            "eos_token": "</s>",
            "pad_token": "</s>",
            "unk_token": "<unk>"
        }
        with open(special_tokens_path, "w") as f:
            json.dump(special_tokens, f, indent=2)
    
    # 5. Create README.md
    readme_path = Path(model_path) / "README.md"
    if not readme_path.exists():
        print("📝 Creating README.md...")
        readme_content = """---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
- mistral
- text-generation
---

# Model Card

## Model Description

This model is a fine-tuned version of Mistral.

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("abdelac/Mistral_Test")
tokenizer = AutoTokenizer.from_pretrained("abdelac/Mistral_Test")

prompt = "Explain machine learning"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))