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| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import torch | |
| from peft import PeftModel | |
| import gradio as gr | |
| model = AutoModelForCausalLM.from_pretrained("internlm/internlm-7b",trust_remote_code=True) | |
| model = PeftModel.from_pretrained(model, "fadliaulawi/internlm-7b-finetuned") | |
| tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-7b", padding_side="left", use_fast = False,trust_remote_code=True) | |
| def generate_prompt( | |
| instruction, input, label | |
| ): | |
| # template = { | |
| # "description": "Template used by Alpaca-LoRA.", | |
| # "prompt_input": "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### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n", | |
| # "prompt_no_input": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:\n", | |
| # "response_split": "### Response:" | |
| # } | |
| # <s>[INST] <<SYS>> | |
| # {{ system_prompt }} | |
| # <</SYS>> | |
| # {{ user_message }} [/INST] | |
| # return '''<s>[INST] <<SYS>>\n{0}\n<</SYS>>\n\n{1} {2} [/INST]'''.format(template['prompt_input'].format(instruction=instruction, input=input), template['response_split'], label) | |
| template = { | |
| "description": "Template used by Alpaca-LoRA.", | |
| "prompt_input": "Di bawah ini adalah instruksi yang menjelaskan tugas, dipasangkan dengan masukan yang memberikan konteks lebih lanjut. Tulis tanggapan yang melengkapi permintaan dengan tepat.\n\n### Instruksi:\n{instruction}\n\n### Masukan:\n{input}", | |
| #"prompt_no_input": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:\n", | |
| "response_split": "### Tanggapan:" | |
| } | |
| if input: | |
| res = template["prompt_input"].format(instruction=instruction, input=input) | |
| #else: | |
| # res = template["prompt_no_input"].format(instruction=instruction) | |
| res = f"{res} \n\n### Tanggapan:\n" | |
| if label: | |
| res = f"{res}{label}" | |
| return res | |
| def user(message, history): | |
| return "", history + [[message, None]] | |
| def generate_and_tokenize_prompt(data_point): | |
| full_prompt = generate_prompt( | |
| data_point["instruction"], | |
| data_point["input"], | |
| data_point["output"], | |
| ) | |
| # print(full_prompt) | |
| # return | |
| cutoff_len = 256 | |
| tokenizer.pad_token = tokenizer.eos_token | |
| result = tokenizer( | |
| full_prompt, | |
| truncation=True, | |
| max_length=cutoff_len, | |
| padding=True, | |
| return_tensors=None, | |
| ) | |
| if (result["input_ids"][-1] != tokenizer.eos_token_id and len(result["input_ids"]) < cutoff_len): | |
| result["input_ids"].append(tokenizer.eos_token_id) | |
| result["attention_mask"].append(1) | |
| # result["labels"] = result["input_ids"].copy() | |
| return result | |
| def bot(history,temperature, max_new_tokens, top_p,top_k): | |
| user_message = history[-1][0] | |
| data = { | |
| 'instruction': "Jika Anda seorang dokter, silakan menjawab pertanyaan medis berdasarkan deskripsi pasien.", | |
| 'input': user_message, | |
| 'output': '' | |
| } | |
| new_user_input_ids = generate_and_tokenize_prompt(data) | |
| # append the new user input tokens to the chat history | |
| bot_input_ids = torch.LongTensor([new_user_input_ids['input_ids']]) | |
| # generate a response | |
| response = model.generate( | |
| input_ids=bot_input_ids, | |
| pad_token_id=tokenizer.eos_token_id, | |
| temperature = float(temperature), | |
| max_new_tokens=max_new_tokens, | |
| top_p=float(top_p), | |
| top_k=top_k, | |
| do_sample=True | |
| ) | |
| # clean up response before returning | |
| response = tokenizer.batch_decode(response, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] | |
| sections = response.split("###") | |
| response = sections[3] | |
| response=response.split("Tanggapan:")[1].strip() | |
| history[-1][1] = response | |
| return history | |
| with gr.Blocks() as demo: | |
| gr.Markdown( | |
| """# ChatDoctor - InternLM 7b 🩺 | |
| A [ChatDoctor - InternLM 7b](https://huggingface.co/fadliaulawi/internlm-7b-finetuned) demo. | |
| From the [InternLM 7b](https://huggingface.co/internlm/internlm-7b) model and finetuned on the Indonesian translation of [ChatDoctor](https://github.com/Kent0n-Li/ChatDoctor) dataset. | |
| """ | |
| ) | |
| chatbot = gr.Chatbot() | |
| msg = gr.Textbox() | |
| submit = gr.Button("Submit") | |
| clear = gr.Button("Clear") | |
| examples = gr.Examples(examples=["Dokter, aku mengalami kelelahan akhir-akhir ini.", "Dokter, aku merasa pusing, lemah dan sakit dada tajam akhir-akhir ini.", | |
| "Dokter, aku merasa sangat depresi akhir-akhir ini dan juga mengalami perubahan suhu tubuhku.", | |
| "Dokter, saya sudah beberapa minggu mengalami suara serak dan tidak kunjung membaik meski sudah minum obat. Apa masalahnya?" | |
| ],inputs=[msg]) | |
| gr.Markdown( | |
| """## Adjust the additional inputs:""" | |
| ) | |
| temperature = gr.Slider(0, 5, value=0.8, step=0.1, label='Temperature',info="Controls randomness, higher values increase diversity.") | |
| max_length = gr.Slider(0, 1024, value=50, step=1, label='Max Length',info="The maximum numbers of output's tokens.") | |
| top_p = gr.Slider(0, 1, value=0.8, step=0.1, label='Top P',info="The cumulative probability cutoff for token selection. Lower values mean sampling from a smaller, more top-weighted nucleus.") | |
| top_k = gr.Slider(0, 50, value=10, step=1, label='Top K',info="Sample from the k most likely next tokens at each step. Lower k focuses on higher probability tokens.") | |
| submit.click(user, [msg, chatbot], [msg, chatbot], queue=False).then( | |
| bot, [chatbot,temperature,max_length,top_p,top_k], chatbot | |
| ) | |
| clear.click(lambda: None, None, chatbot, queue=False) | |
| demo.queue(concurrency_count=100).launch() |