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4ddd7a9 0be43d1 4ddd7a9 0be43d1 4ddd7a9 0be43d1 4ddd7a9 b3e26e8 4ddd7a9 0be43d1 b3e26e8 0be43d1 4ddd7a9 0be43d1 4ddd7a9 0be43d1 4ddd7a9 0be43d1 4ddd7a9 0be43d1 4ddd7a9 0be43d1 | 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 | import subprocess, sys
subprocess.check_call([
sys.executable, "-m", "pip", "install", "--quiet",
"transformers>=4.45.0",
"accelerate>=0.26.0",
"sentencepiece>=0.1.99",
])
import gradio as gr
import torch
from transformers import (
AutoTokenizer,
AutoModelForCausalLM,
TextIteratorStreamer,
)
from threading import Thread
MODEL_ID = "google/gemma-4-31B-it-assistant"
print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
# Manually set Gemma chat template since tokenizer doesn't include one
tokenizer.chat_template = (
"{% for message in messages %}"
"{% if message['role'] == 'user' %}"
"user\n{{ message['content'] }}\n"
"{% elif message['role'] == 'assistant' %}"
"model\n{{ message['content'] }}\n"
"{% endif %}"
"{% endfor %}"
"{% if add_generation_prompt %}model\n{% endif %}"
)
print("Loading model...")
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
dtype=torch.bfloat16,
device_map="auto",
)
model.eval()
print("Model ready.")
def chat(message, history):
messages = []
for user_msg, bot_msg in history:
messages.append({"role": "user", "content": user_msg})
messages.append({"role": "assistant", "content": bot_msg})
messages.append({"role": "user", "content": message})
inputs = tokenizer.apply_chat_template(
messages,
return_tensors="pt",
add_generation_prompt=True,
).to(model.device)
streamer = TextIteratorStreamer(
tokenizer,
skip_prompt=True,
skip_special_tokens=True,
)
thread = Thread(
target=model.generate,
kwargs=dict(
input_ids=inputs,
streamer=streamer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9,
),
)
thread.start()
partial = ""
for token in streamer:
partial += token
yield partial
demo = gr.ChatInterface(
fn=chat,
title="Gemma 4 Assistant",
description="google/gemma-4-31B-it-assistant — streaming enabled",
)
demo.launch() |