ncoder-ai/VibeVoice-Large-AWQ-INT4

Use ncoder-ai/VibeVoice-Large-AWQ instead

This repo holds the AWQ-INT4 Qwen2 LLM weights only, in isolation. It exists so the AWQ-quantized LLM can be composed by hand with a custom VibeVoice base (e.g. a fork, a fine-tune, or a different audio stack).

You almost certainly want the unified drop-in instead: ncoder-ai/VibeVoice-Large-AWQ.

That repo bundles the same AWQ-INT4 LLM with FP16 audio components into one checkpoint — transformers.from_pretrained() loads it directly, no manual graft step. Same speed, same VRAM (~8.4 GB), same audio quality.

from vibevoice.modular.modeling_vibevoice_inference import VibeVoiceForConditionalGenerationInference
import torch

model = VibeVoiceForConditionalGenerationInference.from_pretrained(
    "ncoder-ai/VibeVoice-Large-AWQ",
    torch_dtype=torch.float16,
    device_map="cuda:0",
).eval()

---

If you really need the LLM-only weights

For advanced users hand-grafting the AWQ Qwen2 into a custom base. You provide the FP16 audio stack (acoustic tokenizer, diffusion head, connectors); this repo provides only the quantized language model.

import torch, gc
from vibevoice.modular.modeling_vibevoice_inference import VibeVoiceForConditionalGenerationInference
from awq import AutoAWQForCausalLM

# Your custom FP16 base
model = VibeVoiceForConditionalGenerationInference.from_pretrained(
    "rsxdalv/VibeVoice-Large", torch_dtype=torch.float16, device_map="cuda:0",
).eval()

# Free FP16 LLM, graft AWQ Qwen2 in its place
del model.model.language_model
gc.collect(); torch.cuda.empty_cache()

awq = AutoAWQForCausalLM.from_quantized(
    "ncoder-ai/VibeVoice-Large-AWQ-INT4",
    device_map={"": 0}, safetensors=True, fuse_layers=False,
)
model.model.language_model = awq.model.model
del awq; gc.collect(); torch.cuda.empty_cache()

Quantization recipe: AutoAWQ, 4-bit, group_size=128, GEMM (Marlin) version, zero_point=True. Calibration: 250 samples (200 prose + 50 wikitext), 512-token max length.

License

MIT — same as upstream rsxdalv/VibeVoice-Large.