Spaces:
Runtime error
Runtime error
File size: 3,785 Bytes
b308128 937be2f 7f877a9 b308128 8eb0f9a 7eaa7b0 937be2f 04fc021 35e0ec8 edb0bcd 35e0ec8 edb0bcd 35e0ec8 a450a5f 35e0ec8 a450a5f 35e0ec8 85bd1c9 35e0ec8 8eb0f9a 35e0ec8 8eb0f9a ac4f141 35e0ec8 a450a5f 35e0ec8 5e8be56 8c245db |
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 |
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import time
# Load the Vicuna 7B v1.3 LMSys model and tokenizer
model_name = "lmsys/vicuna-7b-v1.3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
with gr.Blocks() as demo:
gr.Markdown("# LLM Evaluator With Linguistic Scrutiny")
with gr.Tab("POS"):
gr.Markdown(" Description ")
prompt_POS = gr.Textbox(show_label=False, placeholder="Write a prompt and press enter")
gr.Markdown("Strategy 1 QA")
with gr.Row():
vicuna_S1_chatbot_POS = gr.Chatbot(label="vicuna-7b")
llama_S1_chatbot_POS = gr.Chatbot(label="llama-7b")
gpt_S1_chatbot_POS = gr.Chatbot(label="gpt-3.5")
clear = gr.ClearButton([prompt_POS, vicuna_S1_chatbot_POS])
gr.Markdown("Strategy 2 Instruction")
with gr.Row():
vicuna_S2_chatbot_POS = gr.Chatbot(label="vicuna-7b")
llama_S2_chatbot_POS = gr.Chatbot(label="llama-7b")
gpt_S2_chatbot_POS = gr.Chatbot(label="gpt-3.5")
clear = gr.ClearButton([prompt_POS, vicuna_S2_chatbot_POS])
gr.Markdown("Strategy 3 Structured Prompting")
with gr.Row():
vicuna_S3_chatbot_POS = gr.Chatbot(label="vicuna-7b")
llama_S3_chatbot_POS = gr.Chatbot(label="llama-7b")
gpt_S3_chatbot_POS = gr.Chatbot(label="gpt-3.5")
clear = gr.ClearButton([prompt_POS, vicuna_S3_chatbot_POS])
with gr.Tab("Chunk"):
gr.Markdown(" Description ")
prompt_CHUNK = gr.Textbox(show_label=False, placeholder="Write a prompt and press enter")
gr.Markdown("Strategy 1 QA")
with gr.Row():
vicuna_S1_chatbot_CHUNK = gr.Chatbot(label="vicuna-7b")
llama_S1_chatbot_CHUNK = gr.Chatbot(label="llama-7b")
gpt_S1_chatbot_CHUNK = gr.Chatbot(label="gpt-3.5")
clear = gr.ClearButton([prompt_CHUNK, vicuna_S1_chatbot_CHUNK])
gr.Markdown("Strategy 2 Instruction")
with gr.Row():
vicuna_S2_chatbot_CHUNK = gr.Chatbot(label="vicuna-7b")
llama_S2_chatbot_CHUNK = gr.Chatbot(label="llama-7b")
gpt_S2_chatbot_CHUNK = gr.Chatbot(label="gpt-3.5")
clear = gr.ClearButton([prompt_CHUNK, vicuna_S2_chatbot_CHUNK])
gr.Markdown("Strategy 3 Structured Prompting")
with gr.Row():
vicuna_S3_chatbot_CHUNK = gr.Chatbot(label="vicuna-7b")
llama_S3_chatbot_CHUNK = gr.Chatbot(label="llama-7b")
gpt_S3_chatbot_CHUNK = gr.Chatbot(label="gpt-3.5")
clear = gr.ClearButton([prompt_CHUNK, vicuna_S3_chatbot_CHUNK])
def respond(message, chat_history):
input_ids = tokenizer.encode(message, return_tensors="pt")
output_ids = model.generate(input_ids, max_length=50, num_beams=5, no_repeat_ngram_size=2)
bot_message = tokenizer.decode(output_ids[0], skip_special_tokens=True)
chat_history.append((message, bot_message))
time.sleep(2)
return "", chat_history
prompt_POS.submit(respond, [prompt_POS, vicuna_S1_chatbot_POS], [prompt_POS, vicuna_S1_chatbot_POS])
prompt_POS.submit(respond, [prompt_POS, vicuna_S2_chatbot_POS], [prompt_POS, vicuna_S2_chatbot_POS])
prompt_POS.submit(respond, [prompt_POS, vicuna_S3_chatbot_POS], [prompt_POS, vicuna_S3_chatbot_POS])
prompt_CHUNK.submit(respond, [prompt_CHUNK, vicuna_S1_chatbot_CHUNK], [prompt_CHUNK, vicuna_S1_chatbot_CHUNK])
prompt_CHUNK.submit(respond, [prompt_CHUNK, vicuna_S2_chatbot_CHUNK], [prompt_CHUNK, vicuna_S2_chatbot_CHUNK])
prompt_CHUNK.submit(respond, [prompt_CHUNK, vicuna_S3_chatbot_CHUNK], [prompt_CHUNK, vicuna_S3_chatbot_CHUNK])
demo.launch()
|