README.md

---
license: apache-2.0
datasets:
- Abirate/english_quotes
language:
- en
library_name: transformers
---

# Quantization 4Bits - 4.92 GB GPU memory usage for inference:

```
$ nvidia-smi
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 515.105.01   Driver Version: 515.105.01   CUDA Version: 11.7     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   1  NVIDIA GeForce ...  Off  | 00000000:04:00.0 Off |                  N/A |
| 37%   70C    P2   163W / 170W |   4923MiB / 12288MiB |     91%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
```

## Fine-tuning
Details:
```
3 epochs, all dataset samples (split=train), 939 steps
1 x GPU NVidia RTX 3060 12GB - max. GPU memory: 7.44 GB
duration: 1h45min
```

## Inference 
```
import os
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

model_path = "nlpulse/gpt-j-6b-english_quotes"

# tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_path)
tokenizer.pad_token = tokenizer.eos_token

# quantization config
quant_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

# model
model = AutoModelForCausalLM.from_pretrained(model_path, quantization_config=quant_config, device_map={"":0})

# inference
device = "cuda"
text_list = ["Ask not what your country", "Be the change that", "You only live once, but", "I'm selfish, impatient and"]
for text in text_list:
    inputs = tokenizer(text, return_tensors="pt").to(device)
    outputs = model.generate(**inputs, max_new_tokens=60)
    print('>> ', text, " => ", tokenizer.decode(outputs[0], skip_special_tokens=True))

```

## Scripts
[https://github.com/nlpulse-io/sample_codes/tree/main/fine-tuning/peft_quantization_4bits/gptj-6b](https://github.com/nlpulse-io/sample_codes/tree/main/fine-tuning/peft_quantization_4bits/gptj-6b)