---
license: mit
language:
- en
tags:
- text-to-speech
- tts
- vibevoice
- awq
- int4
- quantized
base_model: rsxdalv/VibeVoice-Large
base_model_relation: quantized
library_name: transformers
pipeline_tag: text-to-speech
---

# Use [`ncoder-ai/VibeVoice-Large-AWQ`](https://huggingface.co/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`](https://huggingface.co/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.

```python
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.

```python
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`.