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Running
on
Zero
Running
on
Zero
File size: 2,096 Bytes
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import os
import subprocess
subprocess.check_call([os.sys.executable, "-m", "pip", "install", "-r", "requirements.txt"])
import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
MODEL_ID = "le-llm/gemma-3-12b-it-reasoning"
# Load model & tokenizer
device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
torch_dtype=torch.float16 if device == "cuda" else torch.float32
).to(device)
SYSTEM_PROMPT = "You are a friendly Chatbot."
def respond(
message,
history: list[dict[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
# Build conversation in chat template format
messages = [{"role": "system", "content": system_message}] + history + [
{"role": "user", "content": message}
]
input_text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True # ensures model knows it's assistant's turn
)
inputs = tokenizer(input_text, return_tensors="pt").to(device)
output_ids = model.generate(
**inputs,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
do_sample=True,
eos_token_id=tokenizer.eos_token_id, # stop at EOS
)
# Only return the newly generated assistant message
response = tokenizer.decode(
output_ids[0][inputs["input_ids"].shape[1]:],
skip_special_tokens=True
)
return response
chatbot = gr.ChatInterface(
respond,
type="messages",
additional_inputs=[
gr.Textbox(value=SYSTEM_PROMPT, label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
)
chatbot.launch()
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