Spaces:
Sleeping
Sleeping
File size: 1,836 Bytes
17f26fa dca8735 18bff0d dca8735 18bff0d 17f26fa dca8735 17f26fa dca8735 17f26fa dca8735 17f26fa dca8735 17f26fa dca8735 17f26fa dca8735 17f26fa dca8735 17f26fa dca8735 18bff0d dca8735 |
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 |
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
import gradio as gr
model_id = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
device_map="auto"
)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
def generate_response(message, history, system_prompt, max_tokens, temperature, top_p):
messages = [{"role": "system", "content": system_prompt}]
for user_msg, bot_msg in history:
if user_msg:
messages.append({"role": "user", "content": user_msg})
if bot_msg:
messages.append({"role": "assistant", "content": bot_msg})
messages.append({"role": "user", "content": message})
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(
input_ids=input_ids,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
do_sample=True,
streamer=streamer
)
output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
return output_text.split(system_prompt)[-1].strip()
chat = gr.ChatInterface(
fn=generate_response,
additional_inputs=[
gr.Textbox(label="System Prompt", value="Eres una mentora empática y reflexiva, especializada en acompañar a empleados en su camino hacia el bienestar laboral."),
gr.Slider(label="Max tokens", minimum=64, maximum=1024, value=512),
gr.Slider(label="Temperature", minimum=0.1, maximum=1.5, value=0.7),
gr.Slider(label="Top-p", minimum=0.1, maximum=1.0, value=0.95),
],
)
if __name__ == "__main__":
chat.launch()
|