| # Simple QLoRA Model Inference | |
| This guide demonstrates how to perform inference using a QLoRA (Quantized Low-Rank Adaptation) fine-tuned model with a single code cell. | |
| ## Requirements | |
| - Python 3.7+ | |
| - PyTorch | |
| - Transformers | |
| - PEFT (Parameter-Efficient Fine-Tuning) | |
| - bitsandbytes | |
| Install the required packages: | |
| ``` | |
| pip install torch transformers peft bitsandbytes | |
| ``` | |
| ## Inference Code | |
| Copy and paste the following code into a Python script or Jupyter notebook cell: | |
| ```python | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig | |
| from peft import PeftModel | |
| # Set up model paths | |
| BASE_MODEL_PATH = "path/to/your/base/model" | |
| ADAPTER_PATH = "path/to/your/qlora/adapter" | |
| # Load tokenizer | |
| tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_PATH) | |
| tokenizer.pad_token = tokenizer.eos_token | |
| tokenizer.padding_side = "right" | |
| # Load quantized model with adapter | |
| bnb_config = BitsAndBytesConfig( | |
| load_in_4bit=True, | |
| bnb_4bit_quant_type="nf4", | |
| bnb_4bit_compute_dtype=torch.float16, | |
| ) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| BASE_MODEL_PATH, | |
| quantization_config=bnb_config, | |
| device_map="auto" | |
| ) | |
| model = PeftModel.from_pretrained(model, ADAPTER_PATH) | |
| # Generate text | |
| prompt = "Explain quantum computing in simple terms:" | |
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device) | |
| outputs = model.generate(**inputs, max_new_tokens=100, do_sample=True, temperature=0.7) | |
| generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| print(generated_text) | |
| ``` | |
| ## Usage | |
| 1. Replace `BASE_MODEL_PATH` with the path to your base model. | |
| 2. Replace `ADAPTER_PATH` with the path to your QLoRA adapter. | |
| 3. Modify the `prompt` variable to use your desired input text. | |
| 4. Run the code cell. | |
| ## Customization | |
| - Adjust `max_new_tokens`, `temperature`, and other generation parameters in the `model.generate()` function call to control the output. | |
| ## Troubleshooting | |
| - If you encounter CUDA out-of-memory errors, try reducing `max_new_tokens` or using a smaller model. | |
| - Ensure your GPU drivers and CUDA toolkit are up-to-date. | |
| For more advanced usage or optimizations, refer to the Hugging Face documentation for Transformers and PEFT. |