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# From https://huggingface.co/spaces/huggingface-projects/llama-2-13b-chat
import os
from threading import Thread
from typing import Iterator
import gradio as gr
import spaces
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "8192"))
DESCRIPTION = """
# Chat with Failure 2B Base
Chat with [Failure 2B Base](https://huggingface.co/mrfakename/failure-2b-base).
---
A quick failed experiment at creating a SLM that can code. Based on [Danube](https://huggingface.co/h2oai/h2o-danube-1.8b-base).
Scored 14.8% on HumanEval (FWIW I personally recommend using a quantized 7B model for coding instead of a SLM). Open-sourcing for transparency.
"""
if not torch.cuda.is_available():
DESCRIPTION += "\n\nRunning on CPU 🥶 This demo does not work on CPU."
if torch.cuda.is_available():
model_id = "mrfakename/failure-2b-base"
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.use_default_system_prompt = False
@spaces.GPU
def generate(
message: str,
chat_history: list[tuple[str, str]],
system_prompt: str,
max_new_tokens: int = 1024,
temperature: float = 0.2,
top_p: float = 0.9,
top_k: int = 50,
repetition_penalty: float = 1.2,
) -> Iterator[str]:
conversation = []
if system_prompt:
conversation.append({"role": "system", "content": system_prompt})
for user, assistant in chat_history:
conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
conversation.append({"role": "user", "content": message})
input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt")
if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
input_ids = input_ids.to(model.device)
streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
{"input_ids": input_ids},
streamer=streamer,
max_new_tokens=max_new_tokens,
do_sample=True,
top_p=top_p,
top_k=top_k,
temperature=temperature,
num_beams=1,
repetition_penalty=repetition_penalty,
)
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
outputs = []
for text in streamer:
outputs.append(text)
yield "".join(outputs)
chat_interface = gr.ChatInterface(
fn=generate,
additional_inputs=[
gr.Textbox(label="System prompt", lines=6),
gr.Slider(
label="Max new tokens",
minimum=1,
maximum=MAX_MAX_NEW_TOKENS,
step=1,
value=DEFAULT_MAX_NEW_TOKENS,
),
gr.Slider(
label="Temperature",
minimum=0.1,
maximum=4.0,
step=0.1,
value=0.2,
),
gr.Slider(
label="Top-p (nucleus sampling)",
minimum=0.05,
maximum=1.0,
step=0.05,
value=0.9,
),
gr.Slider(
label="Top-k",
minimum=1,
maximum=1000,
step=1,
value=50,
),
gr.Slider(
label="Repetition penalty",
minimum=1.0,
maximum=2.0,
step=0.05,
value=1.2,
),
],
stop_btn=None,
examples=[
["Hello there! How are you doing?"],
["Please explain the Python programming language to me."],
["Please write a function in Python to calculate the fibonacci sequence."],
["Write a 100-word article on 'Benefits of Open-Source in AI research'"],
],
)
with gr.Blocks() as demo:
gr.Markdown(DESCRIPTION)
chat_interface.render()
if __name__ == "__main__":
demo.queue(max_size=20).launch() |