app.py

import json
import gradio as gr
import spaces
from huggingface_hub import hf_hub_download
from llama_cpp import Llama
from transformers import AutoTokenizer

MAX_NEW_TOKENS = 8192
MODEL_NAME = "Azure99/Blossom-V6.2-36B"
MODEL_GGUF_REPO = f"{MODEL_NAME}-GGUF"
MODEL_FILE = "blossom-v6.2-36b-q8_0.gguf"
MODEL_LOCAL_DIR = "./"

hf_hub_download(repo_id=MODEL_GGUF_REPO, filename=MODEL_FILE, local_dir=MODEL_LOCAL_DIR)

llm: Llama = None
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)


def get_messages(user, history):
    try:
        parsed_body = json.loads(user)
        if parsed_body.get("by_json_str"):
            return parsed_body["messages"]
    except:
        pass

    messages = []
    messages.extend(history or [])
    messages.append({"role": "user", "content": user})
    return messages


@spaces.GPU(duration=120)
def chat(user, history, temperature, top_p, repetition_penalty):
    global llm
    if llm is None:
        llm = Llama(
            model_path=MODEL_FILE, n_gpu_layers=-1, flash_attn=True, n_ctx=16384
        )

    messages = get_messages(user, history)
    print(f"Messages: {messages}")
    input_ids = tokenizer.apply_chat_template(messages)
    generate_config = dict(
        temperature=temperature,
        top_p=top_p,
        repeat_penalty=repetition_penalty,
        top_k=0,
        stream=True,
        max_tokens=MAX_NEW_TOKENS,
    )

    outputs = ""
    for chunk in llm(input_ids, **generate_config):
        outputs += chunk["choices"][0]["text"]
        yield outputs


additional_inputs = [
    gr.Slider(
        label="Temperature",
        value=0.5,
        minimum=0.0,
        maximum=1.0,
        step=0.05,
        interactive=True,
        info="Controls randomness in choosing words.",
    ),
    gr.Slider(
        label="Top-P",
        value=0.85,
        minimum=0.0,
        maximum=1.0,
        step=0.05,
        interactive=True,
        info="Picks words until their combined probability is at least top_p.",
    ),
    gr.Slider(
        label="Repetition penalty",
        value=1.05,
        minimum=1.0,
        maximum=1.2,
        step=0.01,
        interactive=True,
        info="Repetition Penalty: Controls how much repetition is penalized.",
    ),
]

gr.ChatInterface(
    chat,
    type="messages",
    chatbot=gr.Chatbot(
        show_label=False,
        height=500,
        show_copy_button=True,
        render_markdown=True,
        type="messages",
        latex_delimiters=[{"left": "\\[", "right": "\\]", "display": True}],
    ),
    textbox=gr.Textbox(placeholder="", container=False, scale=7),
    title=f"{MODEL_NAME} Demo",
    description="Hello, I am Blossom, an open source conversational large language model.🌠"
    '<a href="https://github.com/Azure99/BlossomLM">GitHub</a>',
    theme="soft",
    examples=[
        ["Hello"],
        ["What is MBTI"],
        ["用Python实现二分查找"],
        ["为switch写一篇小红书种草文案，带上emoji"],
    ],
    cache_examples=False,
    additional_inputs=additional_inputs,
    additional_inputs_accordion=gr.Accordion(label="Config", open=True),
).queue().launch()