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import sys |
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import torch |
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from peft import PeftModel |
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import transformers |
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import gradio as gr |
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from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig |
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tokenizer = AutoTokenizer.from_pretrained("VietAI/gpt-j-6B-vietnamese-news") |
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LOAD_8BIT = True |
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BASE_MODEL = "VietAI/gpt-j-6B-vietnamese-news" |
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LORA_WEIGHTS = "hoaiht/vietnamese-alpaca-lora-gpt-j" |
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if torch.cuda.is_available(): |
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device = "cuda" |
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else: |
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device = "cpu" |
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try: |
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if torch.backends.mps.is_available(): |
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device = "mps" |
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except: |
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pass |
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if device == "cuda": |
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model = AutoModelForCausalLM.from_pretrained( |
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BASE_MODEL, |
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load_in_8bit=LOAD_8BIT, |
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torch_dtype=torch.float16, |
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device_map="auto", |
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) |
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model = PeftModel.from_pretrained( |
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model, |
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LORA_WEIGHTS, |
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torch_dtype=torch.float16, |
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) |
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elif device == "mps": |
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model = transformers.AutoModelForCausalLM.from_pretrained( |
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BASE_MODEL, |
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device_map={"": device}, |
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torch_dtype=torch.float16, |
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) |
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model = PeftModel.from_pretrained( |
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model, |
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LORA_WEIGHTS, |
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device_map={"": device}, |
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torch_dtype=torch.float16, |
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) |
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else: |
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model = transformers.AutoModelForCausalLM.from_pretrained( |
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BASE_MODEL, device_map={"": device}, low_cpu_mem_usage=True |
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) |
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model = PeftModel.from_pretrained( |
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model, |
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LORA_WEIGHTS, |
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device_map={"": device}, |
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) |
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def generate_prompt(instruction, input=None): |
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if input: |
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return f"""Dưới đây là một hướng dẫn mô tả một tác vụ, kèm theo một đầu vào cung cấp thêm ngữ cảnh. Viết một phản hồi hoàn thành yêu cầu một cách thích hợp. |
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### Instruction: |
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{instruction} |
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### Input: |
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{input} |
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### Response:""" |
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else: |
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return f"""Dưới đây là một hướng dẫn mô tả một tác vụ. Viết một phản hồi hoàn thành yêu cầu một cách thích hợp. |
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### Instruction: |
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{instruction} |
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### Response:""" |
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if not LOAD_8BIT: |
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model.half() |
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model.eval() |
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if torch.__version__ >= "2" and sys.platform != "win32": |
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model = torch.compile(model) |
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def evaluate( |
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instruction, |
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input=None, |
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temperature=0.1, |
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top_p=0.75, |
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top_k=40, |
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num_beams=4, |
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max_new_tokens=128, |
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**kwargs, |
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): |
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prompt = generate_prompt(instruction, input) |
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inputs = tokenizer(prompt, return_tensors="pt") |
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input_ids = inputs["input_ids"].to(device) |
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generation_config = GenerationConfig( |
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temperature=temperature, |
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top_p=top_p, |
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top_k=top_k, |
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num_beams=num_beams, |
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**kwargs, |
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) |
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with torch.no_grad(): |
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gen_tokens = model.generate( |
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input_ids=input_ids, |
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max_length=max_new_tokens, |
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do_sample=True, |
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temperature=0.9, |
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top_k=20 |
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) |
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output = tokenizer.batch_decode(gen_tokens)[0] |
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return output.split("### Response:")[1].strip() |
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gr.Interface( |
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fn=evaluate, |
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inputs=[ |
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gr.components.Textbox( |
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lines=2, label="Instruction", value="3 điều cần làm để duy trì sức khỏe." |
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), |
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gr.components.Textbox(lines=2, label="Input", placeholder="none"), |
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gr.components.Slider(minimum=0, maximum=1, value=0.1, label="Temperature"), |
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gr.components.Slider(minimum=0, maximum=1, value=0.75, label="Top p"), |
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gr.components.Slider(minimum=0, maximum=100, step=1, value=40, label="Top k"), |
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gr.components.Slider(minimum=1, maximum=4, step=1, value=4, label="Beams"), |
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gr.components.Slider( |
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minimum=1, maximum=2000, step=1, value=128, label="Max tokens" |
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), |
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], |
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outputs=[ |
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gr.inputs.Textbox( |
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lines=5, |
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label="Output", |
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) |
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], |
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title="🦙🌲 Instruct-tune `VietAI/gpt-j-6B-vietnamese-news` on Alpaca dataset (Vietnamese version) using Alpaca-LoRA", |
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).launch(share=True) |
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""" |
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if __name__ == "__main__": |
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# testing code for readme |
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for instruction in [ |
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"Tell me about alpacas.", |
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"Tell me about the president of Mexico in 2019.", |
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"Tell me about the king of France in 2019.", |
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"List all Canadian provinces in alphabetical order.", |
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"Write a Python program that prints the first 10 Fibonacci numbers.", |
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"Write a program that prints the numbers from 1 to 100. But for multiples of three print 'Fizz' instead of the number and for the multiples of five print 'Buzz'. For numbers which are multiples of both three and five print 'FizzBuzz'.", |
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"Tell me five words that rhyme with 'shock'.", |
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"Translate the sentence 'I have no mouth but I must scream' into Spanish.", |
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"Count up from 1 to 500.", |
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]: |
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print("Instruction:", instruction) |
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print("Response:", evaluate(instruction)) |
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print() |
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""" |
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