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| import gradio as gr | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer | |
| from threading import Thread | |
| def generate_prompt(example: dict) -> str: | |
| """Generates a standardized message to prompt the model with an instruction, optional input and a | |
| 'response' field.""" | |
| if example["input"]: | |
| return ( | |
| "Below is an instruction that describes a task, paired with an input that provides further context. " | |
| "Write a response that appropriately completes the request.\n\n" | |
| f"### Instruction:\n{example['instruction']}\n\n### Input:\n{example['input']}\n\n### Response:" | |
| ) | |
| return ( | |
| "Below is an instruction that describes a task. " | |
| "Write a response that appropriately completes the request.\n\n" | |
| f"### Instruction:\n{example['instruction']}\n\n### Response:" | |
| ) | |
| tokenizer = AutoTokenizer.from_pretrained("mehrdad-es/legalLLM-hf") | |
| model = AutoModelForCausalLM.from_pretrained("mehrdad-es/legalLLM-hf", torch_dtype=torch.float16) | |
| model = model.to('cuda:0') | |
| class StopOnTokens(StoppingCriteria): | |
| def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: | |
| stop_ids = [30, 0] | |
| for stop_id in stop_ids: | |
| if input_ids[0][-1] == stop_id: | |
| return True | |
| return False | |
| def predict(message, history): | |
| prompt,userInput = message.split('!!') | |
| message=generate_prompt({"instruction": prompt, "input": userInput}) | |
| history_transformer_format = history + [[message, ""]] | |
| stop = StopOnTokens() | |
| messages = "".join(["".join(["\n<USER>:"+item[0], "\n<ASSISTANT>:"+item[1]]) #curr_system_message + | |
| for item in history_transformer_format]) | |
| model_inputs = tokenizer([messages], return_tensors="pt").to("cuda") | |
| streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True) | |
| generate_kwargs = dict( | |
| model_inputs, | |
| streamer=streamer, | |
| max_new_tokens=400, | |
| do_sample=True, | |
| top_p=0.85, | |
| top_k=500, | |
| temperature=0.1, | |
| num_beams=1, | |
| stopping_criteria=StoppingCriteriaList([stop]) | |
| ) | |
| t = Thread(target=model.generate, kwargs=generate_kwargs) | |
| t.start() | |
| partial_message = "" | |
| for new_token in streamer: | |
| if new_token != '<': | |
| partial_message += new_token | |
| yield partial_message | |
| gr.ChatInterface(predict).queue().launch() |