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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 |
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MAX_MAX_NEW_TOKENS = 2048 |
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DEFAULT_MAX_NEW_TOKENS = 1024 |
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total_count=0 |
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MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096")) |
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DESCRIPTION = """\ |
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# DeepSeek-6.7B-Chat |
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This Space demonstrates model [DeepSeek-Coder](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct) by DeepSeek, a code model with 6.7B parameters fine-tuned for chat instructions. |
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""" |
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if not torch.cuda.is_available(): |
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DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>" |
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if torch.cuda.is_available(): |
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model_id = "deepseek-ai/deepseek-coder-6.7b-instruct" |
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto") |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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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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chat_history: list[tuple[str, str]], |
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system_prompt: str, |
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max_new_tokens: int = 1024, |
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temperature: float = 0.6, |
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top_p: float = 0.9, |
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top_k: int = 50, |
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repetition_penalty: float = 1, |
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) -> Iterator[str]: |
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global total_count |
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total_count += 1 |
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print(total_count) |
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if total_count % 50 == 0 : |
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os.system("nvidia-smi") |
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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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for user, assistant in chat_history: |
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conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}]) |
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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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streamer = TextIteratorStreamer(tokenizer, timeout=10.0, 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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do_sample=False, |
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top_p=top_p, |
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top_k=top_k, |
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num_beams=1, |
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repetition_penalty=repetition_penalty, |
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eos_token_id=32021 |
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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).replace("<|EOT|>","") |
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chat_interface = gr.ChatInterface( |
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fn=generate, |
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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="Top-p (nucleus sampling)", |
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minimum=0.05, |
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maximum=1.0, |
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step=0.05, |
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value=0.9, |
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), |
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gr.Slider( |
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label="Top-k", |
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minimum=1, |
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maximum=1000, |
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step=1, |
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value=50, |
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), |
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gr.Slider( |
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label="Repetition penalty", |
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minimum=1.0, |
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maximum=2.0, |
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step=0.05, |
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value=1, |
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), |
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], |
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stop_btn=None, |
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examples=[ |
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["implement snake game using pygame"], |
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["Can you explain briefly to me what is the Python programming language?"], |
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["write a program to find the factorial of a number"], |
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], |
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) |
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with gr.Blocks(css="style.css") as demo: |
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gr.Markdown(DESCRIPTION) |
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chat_interface.render() |
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if __name__ == "__main__": |
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demo.queue(max_size=20).launch() |
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