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import os
import gradio as gr
import spaces
import torch
import logging
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers.utils import logging as hf_logging
logging.basicConfig(
filename="/tmp/app.log",
level=logging.DEBUG,
format="%(asctime)s %(levelname)s: %(message)s"
)
logging.info("Starting app.py logging")
hf_logging.set_verbosity_debug()
hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()
hf_logging.add_handler(logging.FileHandler("/tmp/transformers.log"))
model_id = "futurehouse/ether0"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
torch_dtype=torch.float16
)
@spaces.GPU
def chat_fn(prompt, max_tokens=512):
max_tokens = min(int(max_tokens), 32_000)
messages = [{"role": "user", "content": prompt}]
chat_prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
inputs = tokenizer(chat_prompt, return_tensors="pt").to(model.device)
# Generate with proper parameters
outputs = model.generate(
**inputs,
max_new_tokens=max_tokens,
do_sample=True,
temperature=0.1,
pad_token_id=tokenizer.eos_token_id
)
# Decode only the new tokens (not the input)
generated_text = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
return generated_text
gr.Interface(
fn=chat_fn,
inputs=[
gr.Textbox(label="prompt"),
gr.Number(label="max_tokens", value=512, precision=0)
],
outputs="text",
title="Ether0"
).launch(ssr_mode=False) |