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
Commit History
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Remove stray cache files
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Runtime update: released native 1.0.5, memory flags (VAE tile 256, lazy KV handoff), verified-digest cache, background VAE load, chunked INT8 head quantization, compile paths only with --warm-compile; measurements refreshed
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Remove operational evaluation history and versioned harness scripts (kept in the private lab archive)
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Runtime: --mode flag, kernel-binaries.json, evaluation/profiles; drop operational notes and internal version labels
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SHA256SUMS: README
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Card: drop demo narrative
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SHA256SUMS: README
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Card: concise model/run/profiles/measurements
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runtime-lock: OrbitQuant 0.9.8 / native 1.0.5 released (SHA256SUMS)
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runtime-lock: OrbitQuant 0.9.8 / native 1.0.5 released (src/runtime-lock.json)
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Runtime v11: lookahead decode, ABC head crop, resident compiled VAE, KV handoff, INT8/W4A4 output heads; SM89 validated, SM120 built
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Keep only baseline and main fast audio demos
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Record anonymous runtime-v10 verification of both attention layouts
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Add ordered and approximate INT8 attention decode layouts
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Record anonymous runtime-v9 clean-load verification
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Update bundled OrbitQuant with register codebook CUDA decode
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Record anonymous INT8 memory-profile clean-load verification
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Add optional INT8 AR KV cache with measured runtime profiles
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Record clean public native 1.0.3 CFG1.5 CLI validation
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Update packed inference to OrbitQuant0.9.6 and native1.0.3
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Record clean public captured-sampler CLI validation
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Combine decode and sampling with request-owned device history
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Extend request-local captured sampling to ABC planning
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Record clean public captured-sampler CLI validation
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Record anonymous runtime v3 CLI validation
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Publish OrbitQuant 0.9.5 byte-pair decode and optional RMS activation fusion
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Record clean 0.9.4 inference, GPU cost frontier and lower-step experiments
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Upgrade to OrbitQuant 0.9.4 and release discarded layer memory
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Record anonymous clean-load and runtime contract evidence
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Publish measured fused, fast and memory inference profiles with OrbitQuant 0.9.3
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Organize runtime and kernel code under src
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Publish clean-load proof, measurements and Russian listening examples
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Document packed runtime and expose optional AR offload
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Select measured VAE memory profile for release validation
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Select measured VAE memory profile for release validation
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Stage packed YuE2 checkpoint and reproducible runtime for clean-load validation
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initial commit
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