import gradio as gr import torch import transformers # https://github.com/huggingface/peft # Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of pre-trained language models (PLMs) # to various downstream applications without fine-tuning all the model's parameters. from peft import PeftModel from scrape_website import process_webpage assert ( "LlamaTokenizer" in transformers._import_structure["models.llama"] ), "LLaMA is now in HuggingFace's main branch.\nPlease reinstall it: pip uninstall transformers && pip install git+https://github.com/huggingface/transformers.git" from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig tokenizer = LlamaTokenizer.from_pretrained("TheBloke/Llama-2-13B-chat-GPTQ") BASE_MODEL = "TheBloke/Llama-2-13B-chat-GPTQ" LORA_WEIGHTS = "tloen/alpaca-lora-7b" if torch.cuda.is_available(): device = "cuda" else: device = "cpu" try: # mps device enables high-performance training on GPU for MacOS devices with Metal programming framework. if torch.backends.mps.is_available(): device = "mps" except: pass if device == "cuda": model = LlamaForCausalLM.from_pretrained( BASE_MODEL, load_in_8bit=False, torch_dtype=torch.float16, device_map="auto", ) model = PeftModel.from_pretrained( model, LORA_WEIGHTS, torch_dtype=torch.float16, force_download=True ) elif device == "mps": model = LlamaForCausalLM.from_pretrained( BASE_MODEL, device_map={"": device}, torch_dtype=torch.float16, ) model = PeftModel.from_pretrained( model, LORA_WEIGHTS, device_map={"": device}, torch_dtype=torch.float16, ) else: model = LlamaForCausalLM.from_pretrained( BASE_MODEL, device_map={"": device}, low_cpu_mem_usage=True ) model = PeftModel.from_pretrained( model, LORA_WEIGHTS, device_map={"": device}, ) def generate_prompt(instruction, input=None): if input: return f"""Below is an url that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Input: {input} ### Response:""" else: return f"""Below is an url that describes a task. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Response:""" if device != "cpu": model.half() model.eval() if torch.__version__ >= "2": model = torch.compile(model) def evaluate( instruction, url, temperature=0.1, top_p=0.75, top_k=40, num_beams=4, max_new_tokens=128, **kwargs, ): content = process_webpage(url=url) # avoid GPU memory overflow with torch.no_grad(): torch.cuda.empty_cache() prompt = generate_prompt(instruction, content) inputs = tokenizer(prompt, return_tensors="pt") input_ids = inputs["input_ids"].to(device) generation_config = GenerationConfig( temperature=temperature, top_p=top_p, top_k=top_k, num_beams=num_beams, **kwargs, ) generation_output = model.generate( input_ids=input_ids, generation_config=generation_config, return_dict_in_generate=True, output_scores=True, max_new_tokens=max_new_tokens, ) s = generation_output.sequences[0] output = tokenizer.decode(s) # avoid GPU memory overflow torch.cuda.empty_cache() return output.split("### Response:")[1].strip() g = gr.Interface( fn=evaluate, inputs=[ gr.components.Textbox( lines=2, label="FAQ", placeholder="Ask me anything about this website?" ), gr.components.Textbox( lines=1, label="Website URL", placeholder="https://www.meet-drift.ai/" ), # gr.components.Slider(minimum=0, maximum=1, value=0.1, label="Temperature"), # gr.components.Slider(minimum=0, maximum=1, value=0.75, label="Top p"), # gr.components.Slider(minimum=0, maximum=100, step=1, value=40, label="Top k"), # gr.components.Slider(minimum=1, maximum=4, step=1, value=4, label="Beams"), # gr.components.Slider( # minimum=1, maximum=512, step=1, value=128, label="Max tokens" # ), ], outputs=[ gr.inputs.Textbox( lines=5, label="Output", ) ], title="FAQ A Website", examples=[ [ "Can you list the capabilities this company has in bullet points?", "https://www.meet-drift.ai/", ], ["What's the name of the founder?", "https://www.meet-drift.ai/about"], [ "in 1 word what's the service the company is providing?", "https://www.meet-drift.ai/", ], [ "in 1 word what's the service the company is providing?", "https://www.tribe.ai/about", ], ["Who is Noah Gale?", "https://www.tribe.ai/team"], ["What sector is Tribe active in?", "https://www.tribe.ai"], ] # description="Alpaca-LoRA is a 7B-parameter LLaMA model finetuned to follow instructions. It is trained on the [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) dataset and makes use of the Huggingface LLaMA implementation. For more information, please visit [the project's website](https://github.com/tloen/alpaca-lora).", ) g.queue(concurrency_count=1) g.launch()