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import gradio as gr
from llama_cpp import Llama
model = "SanctumAI/Meta-Llama-3-8B-Instruct-GGUF"
llm = Llama.from_pretrained(
repo_id=model,
filename="meta-llama-3-8b-instruct.Q4_K_M.gguf",
verbose=True,
use_mmap=False,
use_mlock=True,
n_threads=2,
n_threads_batch=2,
n_ctx=4000,
)
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
response = llm.create_chat_completion(
messages=messages,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
)
return response["choices"][0]["message"]["content"]
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(
value="You are a helpful assistant.",
label="System message",
),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
description=model,
)
if __name__ == "__main__":
demo.launch()
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