File size: 1,993 Bytes
0c838ce eb56502 0c838ce eb56502 5f84848 4a9f6e0 0c838ce eb56502 0c838ce eb56502 0c838ce eb56502 5f84848 9d34576 0c838ce 51ad018 |
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 |
import gradio as gr
from ctransformers import AutoTokenizer, AutoModelForCausalLM
# model id
model_id = "TheBloke/Mistral-7B-Instruct-v0.1-GGUF"
model_file = "mistral-7b-instruct-v0.1.Q4_K_M.gguf"
model_type = "mistral"
# it's a quantization model
quant_model = AutoModelForCausalLM.from_pretrained(model_id, model_file = model_file , model_type= model_type)
def lechat_respond(
message,
history: list[tuple[str, str]],
max_tokens,
temperature,
top_p,
top_k
):
# so mistral's instruct format . here i didn't use chat history cuase of computation 🙄
text = f"""<s>[INST] {message} [/INST]"""
response = ""
for next_token in quant_model(text,
max_new_tokens = int(max_tokens),
temperature = temperature,
top_p = top_p,
top_k = top_k,
stream = True):
response += next_token
yield response
#chat interface for le_chat
mistral_chat = gr.ChatInterface(
fn = lechat_respond,
type = 'messages',
# chatbot = gr.Chatbot(placeholder = "<h5>LLM running on cpu so it may take long time to respond to ur prompt !</h5>"),
# textbox = gr.Textbox(placeholder= "Ask whatever", scale = 7, container = False),
additional_inputs=[
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.8, step=0.1, label="Temperature"),
gr.Slider(minimum=0.1,maximum=1.0,value=0.95,step=0.05,label="Top-p (nucleus sampling)"),
gr.Slider(minimum = 40, maximum = 10000, value = 40, step = 10, label = "Top-k"),
],
# theme= "ocean",
# examples= [["Write a haiku about destruction of human's and the raise of AI"], ["Which species will rule the Earth in the future"]],
# cache_examples = True
)
if __name__ == "__main__":
mistral_chat.launch()
|