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# 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 = "meta-llama/Meta-Llama-3.1-8B-Instruct"
ADAPTER_PATH = "CCRss/Meta-Llama-3.1-8B-Instruct-qlora-nf-ds_oasst1"
# 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. |