Text Generation
Transformers
Safetensors
English
Chinese
Russian
yue2
music-generation
orbitquant
quantization
4-bit precision
custom-code
8-bit precision
Instructions to use WaveCut/YuE2-3B-OrbitQuant-W4A4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WaveCut/YuE2-3B-OrbitQuant-W4A4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="WaveCut/YuE2-3B-OrbitQuant-W4A4")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("WaveCut/YuE2-3B-OrbitQuant-W4A4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use WaveCut/YuE2-3B-OrbitQuant-W4A4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "WaveCut/YuE2-3B-OrbitQuant-W4A4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "WaveCut/YuE2-3B-OrbitQuant-W4A4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/WaveCut/YuE2-3B-OrbitQuant-W4A4
- SGLang
How to use WaveCut/YuE2-3B-OrbitQuant-W4A4 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "WaveCut/YuE2-3B-OrbitQuant-W4A4" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "WaveCut/YuE2-3B-OrbitQuant-W4A4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "WaveCut/YuE2-3B-OrbitQuant-W4A4" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "WaveCut/YuE2-3B-OrbitQuant-W4A4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use WaveCut/YuE2-3B-OrbitQuant-W4A4 with Docker Model Runner:
docker model run hf.co/WaveCut/YuE2-3B-OrbitQuant-W4A4
File size: 2,423 Bytes
3ea3da7 | 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 | #!/usr/bin/env python3
# SPDX-License-Identifier: Apache-2.0
# From vLLM: https://github.com/vllm-project/vllm/blob/main/cmake/hipify.py
#
# A command line tool for running pytorch's hipify preprocessor on CUDA
# source files.
#
# See https://github.com/ROCm/hipify_torch
# and <torch install dir>/utils/hipify/hipify_python.py
#
import argparse
import os
import shutil
from torch.utils.hipify.hipify_python import hipify
if __name__ == '__main__':
parser = argparse.ArgumentParser()
# Project directory where all the source + include files live.
parser.add_argument(
"-p",
"--project_dir",
help="The project directory.",
)
# Directory where hipified files are written.
parser.add_argument(
"-o",
"--output_dir",
help="The output directory.",
)
# Source files to convert.
parser.add_argument("sources",
help="Source files to hipify.",
nargs="*",
default=[])
args = parser.parse_args()
# Limit include scope to project_dir only
includes = [os.path.join(args.project_dir, '*')]
# Get absolute path for all source files.
extra_files = [os.path.abspath(s) for s in args.sources]
# Copy sources from project directory to output directory.
# The directory might already exist to hold object files so we ignore that.
shutil.copytree(args.project_dir, args.output_dir, dirs_exist_ok=True)
hipify_result = hipify(project_directory=args.project_dir,
output_directory=args.output_dir,
header_include_dirs=[],
includes=includes,
extra_files=extra_files,
show_detailed=True,
is_pytorch_extension=True,
hipify_extra_files_only=True)
hipified_sources = []
for source in args.sources:
s_abs = os.path.abspath(source)
hipified_s_abs = (hipify_result[s_abs].hipified_path if
(s_abs in hipify_result
and hipify_result[s_abs].hipified_path is not None)
else s_abs)
hipified_sources.append(hipified_s_abs)
assert (len(hipified_sources) == len(args.sources))
# Print hipified source files.
print("\n".join(hipified_sources))
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