README_DEPLOY_HF.md

Hugging Face Inference Endpoint: Gemma-3n-E4B-it LoRA Adapter

This repository provides a LoRA adapter fine-tuned on top of a Hugging Face Transformers model (e.g., Gemma-3n-E4B-it) using PEFT. It is ready to be deployed as a Hugging Face Inference Endpoint.

How to Deploy as an Endpoint

1. Upload the adapter directory (produced by training) to your Hugging Face Hub repository.

   • The directory should contain adapter_config.json, adapter_model.bin, and tokenizer files.

2. Add a handler.py file to define the endpoint logic.

3. Push to the Hugging Face Hub.

4. Deploy as an Inference Endpoint via the Hugging Face UI.

---

Example handler.py

This file loads the base model and LoRA adapter, and exposes a __call__ method for inference.

from typing import Dict, Any
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel, PeftConfig
import torch

class EndpointHandler:
    def __init__(self, path="."):
        # Load base model and tokenizer
        base_model_id = "<BASE_MODEL_ID>"  # e.g., "google/gemma-2b"
        self.tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)
        base_model = AutoModelForCausalLM.from_pretrained(base_model_id, trust_remote_code=True)
        # Load LoRA adapter
        self.model = PeftModel.from_pretrained(base_model, f"{path}/adapter")
        self.model.eval()
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model.to(self.device)

    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
        prompt = data["inputs"] if isinstance(data, dict) else data
        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)
        with torch.no_grad():
            output = self.model.generate(**inputs, max_new_tokens=256)
        decoded = self.tokenizer.decode(output[0], skip_special_tokens=True)
        return {"generated_text": decoded}

• Replace <BASE_MODEL_ID> with the correct base model (e.g., google/gemma-2b).
• The endpoint will accept a JSON payload with an inputs field containing the prompt.

---

Notes

• Make sure your requirements.txt includes transformers, peft, and torch.
• For large models, use an Inference Endpoint with GPU.
• You can customize the handler for chat formatting, streaming, etc.

---

Quickstart

1. Train your adapter with train_gemma_unsloth.py.
2. Upload the adapter directory and handler.py to your Hugging Face repo.
3. Deploy as an Inference Endpoint.
4. Send requests to your endpoint!

# Hugging Face Inference Endpoint: Gemma-3n-E4B-it LoRA Adapter

This repository provides a LoRA adapter fine-tuned on top of a Hugging Face Transformers model (e.g., Gemma-3n-E4B-it) using PEFT. It is ready to be deployed as a Hugging Face Inference Endpoint.

## How to Deploy as an Endpoint

1. **Upload the `adapter` directory (produced by training) to your Hugging Face Hub repository.**

   - The directory should contain `adapter_config.json`, `adapter_model.bin`, and tokenizer files.

2. **Add a `handler.py` file to define the endpoint logic.**

3. **Push to the Hugging Face Hub.**

4. **Deploy as an Inference Endpoint via the Hugging Face UI.**

---

## Example `handler.py`

This file loads the base model and LoRA adapter, and exposes a `__call__` method for inference.

```python
from typing import Dict, Any
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel, PeftConfig
import torch

class EndpointHandler:
    def __init__(self, path="."):
        # Load base model and tokenizer
        base_model_id = "<BASE_MODEL_ID>"  # e.g., "google/gemma-2b"
        self.tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)
        base_model = AutoModelForCausalLM.from_pretrained(base_model_id, trust_remote_code=True)
        # Load LoRA adapter
        self.model = PeftModel.from_pretrained(base_model, f"{path}/adapter")
        self.model.eval()
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model.to(self.device)

    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
        prompt = data["inputs"] if isinstance(data, dict) else data
        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)
        with torch.no_grad():
            output = self.model.generate(**inputs, max_new_tokens=256)
        decoded = self.tokenizer.decode(output[0], skip_special_tokens=True)
        return {"generated_text": decoded}

• Replace <BASE_MODEL_ID> with the correct base model (e.g., google/gemma-2b).
• The endpoint will accept a JSON payload with an inputs field containing the prompt.

---

Notes

• Make sure your requirements.txt includes transformers, peft, and torch.
• For large models, use an Inference Endpoint with GPU.
• You can customize the handler for chat formatting, streaming, etc.

---

Quickstart

1. Train your adapter with train_gemma_unsloth.py.
2. Upload the adapter directory and handler.py to your Hugging Face repo.
3. Deploy as an Inference Endpoint.
4. Send requests to your endpoint!