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import os |
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from threading import Thread |
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from typing import Iterator |
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import gradio as gr |
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import spaces |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, AwqConfig |
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MAX_MAX_NEW_TOKENS = 2048 |
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DEFAULT_MAX_NEW_TOKENS = 1024 |
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MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096")) |
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ACCESS_TOKEN = os.getenv("HF_TOKEN", "") |
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model_id = "hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4" |
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quantization_config = AwqConfig( |
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bits=4, |
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fuse_max_seq_len=512, |
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do_fuse=True, |
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) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_id, |
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torch_dtype=torch.float16, |
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device_map="auto", |
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trust_remote_code=True, |
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low_cpu_mem_usage=True, |
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quantization_config=quantization_config, |
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token=ACCESS_TOKEN) |
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tokenizer = AutoTokenizer.from_pretrained( |
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model_id, |
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trust_remote_code=True, |
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token=ACCESS_TOKEN) |
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tokenizer.use_default_system_prompt = False |
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@spaces.GPU |
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def generate( |
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message: str, |
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system_prompt: str, |
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max_new_tokens: int = 1024, |
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temperature: float = 0.01, |
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top_p: float = 0.01, |
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) -> Iterator[str]: |
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conversation = [] |
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if system_prompt: |
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conversation.append({"role": "system", "content": system_prompt}) |
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conversation.append({"role": "user", "content": message}) |
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input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt") |
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if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH: |
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input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:] |
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gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.") |
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input_ids = input_ids.to(model.device) |
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terminators = [ |
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tokenizer.eos_token_id, |
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tokenizer.convert_tokens_to_ids("<|eot_id|>") |
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] |
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streamer = TextIteratorStreamer(tokenizer, timeout=300.0, skip_prompt=True, skip_special_tokens=True) |
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generate_kwargs = dict( |
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{"input_ids": input_ids}, |
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streamer=streamer, |
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max_new_tokens=max_new_tokens, |
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eos_token_id=terminators, |
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do_sample=True, |
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top_p=top_p, |
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temperature=temperature, |
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num_beams=1, |
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) |
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t = Thread(target=model.generate, kwargs=generate_kwargs) |
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t.start() |
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outputs = [] |
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for text in streamer: |
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outputs.append(text) |
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yield "".join(outputs) |
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chat_interface = gr.Interface( |
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fn=generate, |
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inputs=[ |
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gr.Textbox(lines=2, placeholder="Prompt", label="Prompt"), |
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], |
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outputs="text", |
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additional_inputs=[ |
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gr.Textbox(label="System prompt", lines=6), |
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gr.Slider( |
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label="Max new tokens", |
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minimum=1, |
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maximum=MAX_MAX_NEW_TOKENS, |
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step=1, |
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value=DEFAULT_MAX_NEW_TOKENS, |
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), |
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gr.Slider( |
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label="Temperature", |
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minimum=0.1, |
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maximum=4.0, |
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step=0.01, |
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value=0.01, |
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), |
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gr.Slider( |
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label="Top-p (nucleus sampling)", |
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minimum=0.05, |
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maximum=1.0, |
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step=0.01, |
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value=0.01, |
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), |
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], |
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title="Model testing", |
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description="Provide system settings and a prompt to interact with the model.", |
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) |
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chat_interface.queue(max_size=20).launch() |
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