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from transformers import pipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
import gradio as gr
model = "janny127/autotrain-7qmts-cs1er"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = pipeline(
"text-generation",
model=model,
torch_dtype=torch.float32,
device_map="auto",
)
def generate_answer(query, sample_num=3):
formatted_prompt = (
f"<|im_start|>user\n{query}<|im_end|>\n<|im_start|>assistant\n"
)
sequences = pipeline(
formatted_prompt,
do_sample=True,
top_k=50,
top_p = 0.9,
num_return_sequences=sample_num,
repetition_penalty=1.1,
max_new_tokens=150,
eos_token_id=CHAT_EOS_TOKEN_ID,
)
answers = list()
for seq in sequences:
answer = seq['generated_text'].replace(formatted_prompt, "")
answers.append(answer)
#print(f"Result: {answer}")
#print("------------------------------------------")
return answers
interface = gr.ChatInterface(
fn=generate_answer,
stop_btn=None
)
with gr.Blocks() as demo:
interface.render()
demo.launch() |