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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(sysA, sysB, wA, wB, user_message, max_new_tokens, temperature, top_p):
promptA = f"<|system|>{sysA}\n<|user|>{user_message}\n<|assistant|>"
promptB = f"<|system|>{sysB}\n<|user|>{user_message}\n<|assistant|>"
idsA = tokenizer(promptA, return_tensors="pt").input_ids.to(model.device)
idsB = tokenizer(promptB, return_tensors="pt").input_ids.to(model.device)
outA, outB = idsA.clone(), idsB.clone()
response = ""
for _ in range(max_new_tokens):
with torch.no_grad():
logitsA = model(input_ids=outA).logits[:, -1, :]
logitsB = model(input_ids=outB).logits[:, -1, :]
blended = wA * logitsA + wB * logitsB
blended = blended / temperature
probs = F.softmax(blended, dim=-1)
sorted_probs, sorted_idx = torch.sort(probs, descending=True)
cum = torch.cumsum(sorted_probs, dim=-1)
sorted_probs[cum > top_p] = 0
sorted_probs = sorted_probs / sorted_probs.sum(dim=-1, keepdim=True)
token = sorted_idx[:, torch.multinomial(sorted_probs, 1)].squeeze()
outA = torch.cat([outA, token.unsqueeze(0).unsqueeze(0)], dim=1)
outB = torch.cat([outB, token.unsqueeze(0).unsqueeze(0)], dim=1)
token_str = tokenizer.decode(token)
response += token_str
yield response
if token.item() == tokenizer.eos_token_id:
break
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,
stream=True
)
if __name__ == "__main__":
demo.launch()
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