Spaces:
Sleeping
Sleeping
File size: 2,334 Bytes
e95ad42 136e821 e95ad42 acfdbf2 e95ad42 136e821 e95ad42 1c670bf acfdbf2 e95ad42 136e821 ba8ad86 136e821 e03ccf8 acfdbf2 e03ccf8 136e821 e03ccf8 acfdbf2 136e821 |
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 |
import spaces
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import subprocess
import os
def install_cuda_toolkit(): #Swiftly Provided by https://huggingface.co/John6666 to fix OSError: CUDA_HOME environment variable is not set. Please set it to your CUDA install root.
# CUDA_TOOLKIT_URL = "https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run"
CUDA_TOOLKIT_URL = "https://developer.download.nvidia.com/compute/cuda/12.2.0/local_installers/cuda_12.2.0_535.54.03_linux.run"
CUDA_TOOLKIT_FILE = "/tmp/%s" % os.path.basename(CUDA_TOOLKIT_URL)
subprocess.call(["wget", "-q", CUDA_TOOLKIT_URL, "-O", CUDA_TOOLKIT_FILE])
subprocess.call(["chmod", "+x", CUDA_TOOLKIT_FILE])
subprocess.call([CUDA_TOOLKIT_FILE, "--silent", "--toolkit"])
os.environ["CUDA_HOME"] = "/usr/local/cuda"
os.environ["PATH"] = "%s/bin:%s" % (os.environ["CUDA_HOME"], os.environ["PATH"])
os.environ["LD_LIBRARY_PATH"] = "%s/lib:%s" % (
os.environ["CUDA_HOME"],
"" if "LD_LIBRARY_PATH" not in os.environ else os.environ["LD_LIBRARY_PATH"],
)
# Fix: arch_list[-1] += '+PTX'; IndexError: list index out of range
os.environ["TORCH_CUDA_ARCH_LIST"] = "8.0;8.6"
install_cuda_toolkit() #Swiftly Provided by https://huggingface.co/John6666 to fix OSError: CUDA_HOME environment variable is not set. Please set it to your CUDA install root.
device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = AutoTokenizer.from_pretrained("ISTA-DASLab/Meta-Llama-3.1-70B-AQLM-PV-2Bit-1x16")
model = AutoModelForCausalLM.from_pretrained("ISTA-DASLab/Meta-Llama-3.1-70B-AQLM-PV-2Bit-1x16", torch_dtype='auto', device_map='auto').to(device)
@spaces.GPU
def generate_text(prompt):
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(inputs.input_ids) #, max_length=100)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
interface = gr.Interface(
fn=generate_text,
inputs="text",
outputs="text",
title="Meta-Llama-3.1-70B-AQLM-PV-2Bit-1x16 Text Generation",
description="Enter a prompt and generate text using Meta-Llama-3.1-70B-AQLM-PV-2Bit-1x16. Responses are a little bit different Meta-Llama-3.1-70B",
)
interface.launch() |