Spaces:
Sleeping
Sleeping
File size: 2,813 Bytes
36942d4 a7a20a5 39c555f f5f805b 39c555f a7a20a5 39c555f a7a20a5 39c555f a7a20a5 39c555f a7a20a5 39c555f a7a20a5 39c555f a7a20a5 39c555f eaff982 341bd22 f5f805b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 |
import os
import threading
from collections import defaultdict
import gradio as gr
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
TextIteratorStreamer,
)
# Define model paths
model_name_to_path = {
"LeCarnet-3M": "MaxLSB/LeCarnet-3M",
"LeCarnet-8M": "MaxLSB/LeCarnet-8M",
"LeCarnet-21M": "MaxLSB/LeCarnet-21M",
}
# Load Hugging Face token
hf_token = os.environ["HUGGINGFACEHUB_API_TOKEN"]
# Preload models and tokenizers
loaded_models = defaultdict(dict)
for name, path in model_name_to_path.items():
loaded_models[name]["tokenizer"] = AutoTokenizer.from_pretrained(path, token=hf_token)
loaded_models[name]["model"] = AutoModelForCausalLM.from_pretrained(path, token=hf_token)
loaded_models[name]["model"].eval()
def respond(
prompt: str,
chat_history,
model_name: str,
max_tokens: int,
temperature: float,
top_p: float,
):
# Select the appropriate model and tokenizer
tokenizer = loaded_models[model_name]["tokenizer"]
model = loaded_models[model_name]["model"]
# Tokenize input
inputs = tokenizer(prompt, return_tensors="pt")
# Set up streaming
streamer = TextIteratorStreamer(
tokenizer,
skip_prompt=False,
skip_special_tokens=True,
)
# Configure generation parameters
generate_kwargs = dict(
**inputs,
streamer=streamer,
max_new_tokens=max_tokens,
do_sample=True,
temperature=temperature,
top_p=top_p,
eos_token_id=tokenizer.eos_token_id,
)
# Run generation in a background thread
thread = threading.Thread(target=model.generate, kwargs=generate_kwargs)
thread.start()
# Stream results
accumulated = ""
for new_text in streamer:
accumulated += new_text
yield accumulated
# Create Gradio Chat Interface
demo = gr.ChatInterface(
fn=respond,
additional_inputs=[
gr.Dropdown(
choices=["LeCarnet-3M", "LeCarnet-8M", "LeCarnet-21M"],
value="LeCarnet-8M",
label="Model",
),
gr.Slider(1, 512, value=512, step=1, label="Max New Tokens"),
gr.Slider(0.1, 2.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(0.1, 1.0, value=0.9, step=0.05, label="Top-p"),
],
title="LeCarnet",
description="Select a model and enter text to get started.",
examples=[
["Il était une fois un petit garçon qui vivait dans un village paisible."],
["Il était une fois une grenouille qui rêvait de toucher les étoiles chaque nuit depuis son étang."],
["Il était une fois un petit lapin perdu"],
],
cache_examples=False,
)
if __name__ == "__main__":
demo.queue(default_concurrency_limit=10, max_size=10).launch(ssr_mode=False, max_threads=10) |