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--- |
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license: apache-2.0 |
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datasets: |
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- Abirate/english_quotes |
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language: |
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- en |
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library_name: transformers |
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--- |
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|
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# Quantization 4Bits - 4.92 GB GPU memory usage for inference: |
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|
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** Vide same fine-tuning for Llama2-7B-Chat: [https://huggingface.co./nlpulse/llama2-7b-chat-english_quotes](https://huggingface.co./nlpulse/llama2-7b-chat-english_quotes) |
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|
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``` |
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$ nvidia-smi |
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+-----------------------------------------------------------------------------+ |
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| NVIDIA-SMI 515.105.01 Driver Version: 515.105.01 CUDA Version: 11.7 | |
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|-------------------------------+----------------------+----------------------+ |
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| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | |
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| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |
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| | | MIG M. | |
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|===============================+======================+======================| |
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| 1 NVIDIA GeForce ... Off | 00000000:04:00.0 Off | N/A | |
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| 37% 70C P2 163W / 170W | 4923MiB / 12288MiB | 91% Default | |
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| | | N/A | |
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+-------------------------------+----------------------+----------------------+ |
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``` |
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|
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## Fine-tuning |
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``` |
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3 epochs, all dataset samples (split=train), 939 steps |
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1 x GPU NVidia RTX 3060 12GB - max. GPU memory: 7.44 GB |
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Duration: 1h45min |
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|
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$ nvidia-smi && free -h |
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+-----------------------------------------------------------------------------+ |
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| NVIDIA-SMI 515.105.01 Driver Version: 515.105.01 CUDA Version: 11.7 | |
|
|-------------------------------+----------------------+----------------------+ |
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| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | |
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| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |
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| | | MIG M. | |
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|===============================+======================+======================| |
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| 1 NVIDIA GeForce ... Off | 00000000:04:00.0 Off | N/A | |
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|100% 89C P2 166W / 170W | 7439MiB / 12288MiB | 93% Default | |
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| | | N/A | |
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+-------------------------------+----------------------+----------------------+ |
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total used free shared buff/cache available |
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Mem: 77Gi 14Gi 23Gi 79Mi 39Gi 62Gi |
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Swap: 37Gi 0B 37Gi |
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|
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``` |
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|
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## Inference |
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``` |
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import os |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig |
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|
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model_path = "nlpulse/gpt-j-6b-english_quotes" |
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|
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# tokenizer |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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tokenizer.pad_token = tokenizer.eos_token |
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|
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# quantization config |
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quant_config = BitsAndBytesConfig( |
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load_in_4bit=True, |
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bnb_4bit_use_double_quant=True, |
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bnb_4bit_quant_type="nf4", |
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bnb_4bit_compute_dtype=torch.bfloat16 |
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) |
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|
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# model |
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model = AutoModelForCausalLM.from_pretrained(model_path, quantization_config=quant_config, device_map={"":0}) |
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|
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# inference |
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device = "cuda" |
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text_list = ["Ask not what your country", "Be the change that", "You only live once, but", "I'm selfish, impatient and"] |
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for text in text_list: |
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inputs = tokenizer(text, return_tensors="pt").to(device) |
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outputs = model.generate(**inputs, max_new_tokens=60) |
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print('>> ', text, " => ", tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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|
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``` |
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## Requirements |
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``` |
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pip install -U bitsandbytes |
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pip install -U git+https://github.com/huggingface/transformers.git |
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pip install -U git+https://github.com/huggingface/peft.git |
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pip install -U accelerate |
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pip install -U datasets |
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pip install -U scipy |
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``` |
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|
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## Scripts |
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[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) |
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|
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## References |
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[QLoRa: Fine-Tune a Large Language Model on Your GPU](https://towardsdatascience.com/qlora-fine-tune-a-large-language-model-on-your-gpu-27bed5a03e2b) |
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|
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[Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA](https://huggingface.co./blog/4bit-transformers-bitsandbytes) |
