File size: 2,787 Bytes
b8c24aa 3a82207 63b82b4 c8fdb3b 3a82207 4e81072 7dc3087 4698b78 08c1bd3 4e81072 7dc3087 63b82b4 4698b78 63b82b4 c0e6ae1 ea9c0d3 7115ad7 ea9c0d3 7dc3087 64d8a64 63b82b4 64d8a64 63b82b4 64d8a64 63b82b4 4698b78 63b82b4 08c1bd3 a6b8174 ea9c0d3 3a82207 a6b8174 63b82b4 3a82207 63b82b4 3a82207 63b82b4 ea9c0d3 3a82207 ea9c0d3 3a82207 4698b78 3a82207 63b82b4 e2534da 63b82b4 2cdab2a 63b82b4 ea9c0d3 63b82b4 9a34670 63b82b4 4698b78 63b82b4 4698b78 |
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 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 |
import gradio as gr
import torch
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
TextIteratorStreamer,
)
import os
from threading import Thread
import spaces
import time
import subprocess
subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
token = os.environ["HF_TOKEN"]
model = AutoModelForCausalLM.from_pretrained(
"microsoft/Phi-3-mini-4k-instruct", token=token,trust_remote_code=True
)
tok = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct", token=token)
terminators = [
tok.eos_token_id,
]
if torch.cuda.is_available():
device = torch.device("cuda")
print(f"Using GPU: {torch.cuda.get_device_name(device)}")
else:
device = torch.device("cpu")
print("Using CPU")
model = model.to(device)
# Dispatch Errors
@spaces.GPU(duration=60)
def chat(message, history, temperature,do_sample, max_tokens):
chat = []
for item in history:
chat.append({"role": "user", "content": item[0]})
if item[1] is not None:
chat.append({"role": "assistant", "content": item[1]})
chat.append({"role": "user", "content": message})
messages = tok.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
model_inputs = tok([messages], return_tensors="pt").to(device)
streamer = TextIteratorStreamer(
tok, timeout=20.0, skip_prompt=True, skip_special_tokens=True
)
generate_kwargs = dict(
model_inputs,
streamer=streamer,
max_new_tokens=max_tokens,
do_sample=True,
temperature=temperature,
eos_token_id=terminators,
)
if temperature == 0:
generate_kwargs['do_sample'] = False
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
partial_text = ""
for new_text in streamer:
partial_text += new_text
yield partial_text
yield partial_text
demo = gr.ChatInterface(
fn=chat,
examples=[["Write me a poem about Machine Learning."]],
# multimodal=False,
additional_inputs_accordion=gr.Accordion(
label="⚙️ Parameters", open=False, render=False
),
additional_inputs=[
gr.Slider(
minimum=0, maximum=1, step=0.1, value=0.9, label="Temperature", render=False
),
gr.Checkbox(label="Sampling",value=True),
gr.Slider(
minimum=128,
maximum=4096,
step=1,
value=512,
label="Max new tokens",
render=False,
),
],
stop_btn="Stop Generation",
title="Chat With LLMs",
description="Now Running [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)"
)
demo.launch() |