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import gradio as gr
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModelForCausalLM
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
)
model.eval()
def blend_generate(prompt, wa, wb):
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
with torch.no_grad():
output_a = model_a(input_ids)
output_b = model_b(input_ids)
logits_a = output_a.logits[:, -1, :]
logits_b = output_b.logits[:, -1, :]
# Weighted sum of raw logits (before softmax)
blended_logits = wa * logits_a + wb * logits_b
# Apply softmax safely to get valid probability distribution
probs = torch.softmax(blended_logits, dim=-1)
# Sample token from valid probability distribution
token = torch.multinomial(probs, 1)
next_token_id = token.item()
next_token = tokenizer.decode([next_token_id])
return next_token
with gr.Blocks() as demo:
gr.Markdown("## Blended Prompt Chat (TinyLlama)")
sysA = gr.Textbox(label="System Prompt A", value="You are assistant A.")
sysB = gr.Textbox(label="System Prompt B", value="You are assistant B.")
wA = gr.Slider(-5, 5, value=1.0, step=0.1, label="Weight A")
wB = gr.Slider(-5, 5, value=1.0, step=0.1, label="Weight B")
user_msg = gr.Textbox(label="User Message")
temp = gr.Slider(0.1, 2.0, value=1.0, step=0.1, label="Temperature")
top_p = gr.Slider(0.1, 1.0, value=0.9, step=0.05, label="Top-p")
max_tokens = gr.Slider(1, 200, value=100, step=1, label="Max New Tokens")
output = gr.Textbox(label="Response")
btn = gr.Button("Generate")
btn.click(
blend_generate,
[sysA, sysB, wA, wB, user_msg, max_tokens, temp, top_p],
output,
show_progress=True,
)
if __name__ == "__main__":
demo.launch()
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