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import os | |
import time | |
import spaces | |
import torch | |
import gradio as gr | |
from threading import Thread | |
HF_TOKEN = os.environ.get("HF_TOKEN", None) | |
TITLE = "<h1><center>Mistral-lab</center></h1>" | |
PLACEHOLDER = """ | |
<center> | |
<p>Chat with Mistral AI LLM.</p> | |
</center> | |
""" | |
CSS = """ | |
.duplicate-button { | |
margin: auto !important; | |
color: white !important; | |
background: black !important; | |
border-radius: 100vh !important; | |
} | |
h3 { | |
text-align: center; | |
} | |
""" | |
from huggingface_hub import snapshot_download | |
from pathlib import Path | |
mistral_models_path = Path.home().joinpath('mistral_models', '8B-Instruct') | |
mistral_models_path.mkdir(parents=True, exist_ok=True) | |
snapshot_download(repo_id="mistralai/Ministral-8B-Instruct-2410", allow_patterns=["params.json", "consolidated.safetensors", "tekken.json"], local_dir=mistral_models_path) | |
from mistral_inference.transformer import Transformer | |
from mistral_inference.generate import generate | |
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer | |
from mistral_common.protocol.instruct.messages import AssistantMessage, UserMessage | |
from mistral_common.protocol.instruct.request import ChatCompletionRequest | |
device = "cuda" # for GPU usage or "cpu" for CPU usage | |
tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tekken.json") | |
model = Transformer.from_folder(mistral_models_path) | |
def stream_chat( | |
message: str, | |
history: list, | |
temperature: float = 0.3, | |
max_new_tokens: int = 1024, | |
top_p: float = 1.0, | |
top_k: int = 20, | |
penalty: float = 1.2, | |
): | |
print(f'message: {message}') | |
print(f'history: {history}') | |
conversation = [] | |
for prompt, answer in history: | |
conversation.append(UserMessage(content=prompt)) | |
conversation.append(AssistantMessage(content=answer)) | |
conversation.append(UserMessage(content=message)) | |
completion_request = ChatCompletionRequest(messages=conversation) | |
tokens = tokenizer.encode_chat_completion(completion_request).tokens | |
out_tokens, _ = generate( | |
[tokens], | |
model, | |
max_tokens=max_new_tokens, | |
temperature=temperature, | |
top_p = top_p, | |
top_k = top_k, | |
repetition_penalty=penalty, | |
eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id) | |
result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0]) | |
return result | |
chatbot = gr.Chatbot(height=600, placeholder=PLACEHOLDER) | |
with gr.Blocks(css=CSS, theme="ocean") as demo: | |
gr.HTML(TITLE) | |
gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button") | |
gr.ChatInterface( | |
fn=stream_chat, | |
chatbot=chatbot, | |
fill_height=True, | |
additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False), | |
additional_inputs=[ | |
gr.Slider( | |
minimum=0, | |
maximum=1, | |
step=0.1, | |
value=0.3, | |
label="Temperature", | |
render=False, | |
), | |
gr.Slider( | |
minimum=128, | |
maximum=8192, | |
step=1, | |
value=1024, | |
label="Max new tokens", | |
render=False, | |
), | |
gr.Slider( | |
minimum=0.0, | |
maximum=1.0, | |
step=0.1, | |
value=1.0, | |
label="top_p", | |
render=False, | |
), | |
gr.Slider( | |
minimum=1, | |
maximum=20, | |
step=1, | |
value=20, | |
label="top_k", | |
render=False, | |
), | |
gr.Slider( | |
minimum=0.0, | |
maximum=2.0, | |
step=0.1, | |
value=1.2, | |
label="Repetition penalty", | |
render=False, | |
), | |
], | |
examples=[ | |
["Help me study vocabulary: write a sentence for me to fill in the blank, and I'll try to pick the correct option."], | |
["What are 5 creative things I could do with my kids' art? I don't want to throw them away, but it's also so much clutter."], | |
["Tell me a random fun fact about the Roman Empire."], | |
["Show me a code snippet of a website's sticky header in CSS and JavaScript."], | |
], | |
cache_examples=False, | |
) | |
if __name__ == "__main__": | |
demo.launch() | |