Spaces:
Runtime error
Runtime error
| # -------------------------------- | |
| # by mrZ | |
| # Email: mrZ@mrZLab630.pw | |
| # Date: 2023-06-29 | |
| # Time: 11:08 | |
| # About: | |
| # -------------------------------- | |
| import sys | |
| import torch | |
| from peft import PeftModel | |
| import transformers | |
| import gradio as gr | |
| 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 | |
| SHARE_GRADIO=True | |
| LOAD_8BIT = False | |
| BASE_MODEL = "mrzlab630/weights_Llama_7b" | |
| LORA_WEIGHTS = "mrzlab630/lora-alpaca-trading-candles" | |
| #BASE_MODEL = "decapoda-research/llama-7b-hf" | |
| tokenizer = LlamaTokenizer.from_pretrained(BASE_MODEL) | |
| 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", | |
| ) | |
| model = PeftModel.from_pretrained( | |
| model, | |
| LORA_WEIGHTS, | |
| torch_dtype=torch.float16, | |
| ) | |
| 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 instruction 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 instruction that describes a task. Write a response that appropriately completes the request. | |
| ### 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 evaluate( | |
| instruction, | |
| input=None, | |
| temperature=0.1, | |
| top_p=0.75, | |
| top_k=40, | |
| num_beams=4, | |
| max_new_tokens=128, | |
| **kwargs, | |
| ): | |
| prompt = generate_prompt(instruction, 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, | |
| ) | |
| s = generation_output.sequences[0] | |
| output = tokenizer.decode(s) | |
| return output.split("### Response:")[1].strip() | |
| gr.Interface( | |
| fn=evaluate, | |
| inputs=[ | |
| gr.components.Textbox( | |
| lines=2, label="Instruction", placeholder="Tell me about alpacas." | |
| ), | |
| gr.components.Textbox(lines=2, label="Input", placeholder="none"), | |
| 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=2000, step=1, value=128, label="Max tokens" | |
| ), | |
| ], | |
| outputs=[ | |
| gr.inputs.Textbox( | |
| lines=5, | |
| label="Output", | |
| ) | |
| ], | |
| title="💹 🕯 Alpaca-LoRA-Trading-Candles", | |
| description="Alpaca-LoRA-Trading-Candles is a 7B-parameter LLaMA model tuned to execute instructions. It is trained on the [trading candles dataset](https://huggingface.co/datasets/mrzlab630/trading-candles) and uses the Huggingface LLaMA implementation. For more information, visit [project website](https://huggingface.co/mrzlab630/lora-alpaca-trading-candles).", | |
| ).launch(server_name="0.0.0.0", share=SHARE_GRADIO) |