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| import sys | |
| import torch | |
| from peft import PeftModel | |
| import transformers | |
| import gradio as gr | |
| import argparse | |
| import warnings | |
| import os | |
| 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 | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--model_path", type=str, default="decapoda-research/llama-7b-hf") | |
| parser.add_argument("--lora_path", type=str, default="./lora-Vicuna/checkpoint-final") | |
| parser.add_argument("--use_local", type=int, default=1) | |
| args = parser.parse_args() | |
| tokenizer = LlamaTokenizer.from_pretrained(args.model_path) | |
| LOAD_8BIT = True | |
| BASE_MODEL = args.model_path | |
| LORA_WEIGHTS = args.lora_path | |
| # fix the path for local checkpoint | |
| lora_bin_path = os.path.join(args.lora_path, "adapter_model.bin") | |
| print(lora_bin_path) | |
| if not os.path.exists(lora_bin_path) and args.use_local: | |
| pytorch_bin_path = os.path.join(args.lora_path, "pytorch_model.bin") | |
| print(pytorch_bin_path) | |
| if os.path.exists(pytorch_bin_path): | |
| os.rename(pytorch_bin_path, lora_bin_path) | |
| warnings.warn("The file name of the lora checkpoint'pytorch_model.bin' is replaced with 'adapter_model.bin'") | |
| else: | |
| assert ('Checkpoint is not Found!') | |
| if torch.cuda.is_available(): | |
| device = "cuda" | |
| else: | |
| device = "cpu" | |
| try: | |
| if torch.backends.mps.is_available(): | |
| device = "mps" | |
| except: | |
| pass | |
| if device == "cuda": | |
| model = LlamaForCausalLM.from_pretrained( | |
| BASE_MODEL, | |
| load_in_8bit=LOAD_8BIT, | |
| torch_dtype=torch.float16, | |
| device_map="auto", #device_map={"": 0}, | |
| ) | |
| model = PeftModel.from_pretrained( | |
| model, | |
| LORA_WEIGHTS, | |
| torch_dtype=torch.float16, | |
| device_map="auto", #device_map={"": 0}, | |
| ) | |
| 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"""The following is a conversation between an AI assistant called Assistant and a human user called User. | |
| ### Instruction: | |
| {instruction} | |
| ### Input: | |
| {input} | |
| ### Response:""" | |
| else: | |
| return f"""The following is a conversation between an AI assistant called Assistant and a human user called User. | |
| ### Instruction: | |
| {instruction} | |
| ### Response:""" | |
| if not LOAD_8BIT: | |
| model.half() # seems to fix bugs for some users. | |
| model.eval() | |
| if torch.__version__ >= "2" and sys.platform != "win32": | |
| model = torch.compile(model) | |
| def interaction( | |
| input, | |
| history, | |
| temperature=0.1, | |
| top_p=0.75, | |
| top_k=40, | |
| num_beams=4, | |
| max_new_tokens=128, | |
| repetition_penalty=1.0, | |
| max_memory=256, | |
| **kwargs, | |
| ): | |
| now_input = input | |
| history = history or [] | |
| if len(history) != 0: | |
| input = "\n".join(["User:" + i[0]+"\n"+"Assistant:" + i[1] for i in history]) + "\n" + "User:" + input | |
| if len(input) > max_memory: | |
| input = input[-max_memory:] | |
| print(input) | |
| print(len(input)) | |
| prompt = generate_prompt(input) | |
| 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, | |
| ) | |
| with torch.no_grad(): | |
| 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, | |
| repetition_penalty=float(repetition_penalty), | |
| ) | |
| s = generation_output.sequences[0] | |
| output = tokenizer.decode(s) | |
| output = output.split("### Response:")[1].strip() | |
| output = output.replace("Belle", "Vicuna") | |
| if 'User:' in output: | |
| output = output.split("User:")[0] | |
| history.append((now_input, output)) | |
| print(history) | |
| return history, history | |
| chatbot = gr.Chatbot().style(color_map=("green", "pink")) | |
| demo = gr.Interface( | |
| fn=interaction, | |
| inputs=[ | |
| gr.components.Textbox( | |
| lines=2, label="Input", placeholder="Tell me about alpacas." | |
| ), | |
| "state", | |
| gr.components.Slider(minimum=0, maximum=1, value=1.0, label="Temperature"), | |
| gr.components.Slider(minimum=0, maximum=1, value=0.9, label="Top p"), | |
| gr.components.Slider(minimum=0, maximum=100, step=1, value=60, label="Top k"), | |
| gr.components.Slider(minimum=1, maximum=5, step=1, value=2, label="Beams"), | |
| gr.components.Slider( | |
| minimum=1, maximum=2000, step=1, value=128, label="Max new tokens" | |
| ), | |
| gr.components.Slider( | |
| minimum=0.1, maximum=10.0, step=0.1, value=2.0, label="Repetition Penalty" | |
| ), | |
| gr.components.Slider( | |
| minimum=0, maximum=2000, step=1, value=256, label="max memory" | |
| ), | |
| ], | |
| outputs=[chatbot, "state"], | |
| allow_flagging="auto", | |
| title="Chinese-Vicuna 中文小羊驼", | |
| description="中文小羊驼由各种高质量的开源instruction数据集，结合Alpaca-lora的代码训练而来，模型基于开源的llama7B，主要贡献是对应的lora模型。由于代码训练资源要求较小，希望为llama中文lora社区做一份贡献。", | |
| ) | |
| demo.queue().launch(share=True, inbrowser=True) |