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Community quantization. This is an unofficial Q4_K quantization of NVIDIA PersonaPlex-7B-v1 for browser-based inference via WebGPU. Not affiliated with or endorsed by NVIDIA.
| Original (bf16) | This model (Q4_K) | |
|---|---|---|
| Temporal layers | 32 | 32 |
| Total params | 8.37B | 8.37B |
| File size | 16.7 GB | 4.4 GB |
| Format | safetensors | GGUF Q4_K |
| File | Size | Description |
|---|---|---|
shards/personaplex-7b-v1-q4_k.gguf.shard-{00-08} | 4.4 GB total | Q4_K weights, sharded (<512 MB each for WASM ArrayBuffer limit) |
tokenizer-e351c8d8-checkpoint125.safetensors | 367 MB | Mimi audio codec weights |
tokenizer_spm_32k_3.model | 540 KB | SentencePiece text tokenizer |
voices/*.pt | ~330 KB each | 18 voice prompt embeddings, PyTorch format |
voices/*.embeddings.bin | ~800 KB each | Same embeddings as raw f32le (for web demo) |
voices/*.cache.json | ~1 KB each | Token cache snapshots for voice conditioning (for web demo) |
config.json | Model architecture metadata |
.pt — PyTorch tensor (embeddings + cache, bfloat16).embeddings.bin — raw f32 little-endian embeddings, shape [num_frames, 4096] (for browser use).cache.json — token cache snapshot, 17 streams × 4 positions (for browser use)Temporal Transformer (32 layers)
dim: 4096, heads: 32, ff: 11264
RoPE positional encoding (freq_base=10000)
Depth Transformer (6 layers)
dim: 1024, heads: 16, ff: 2816
16 codebook-specific gating modules
Audio: 8 codebooks (Mimi codec, 12.5 Hz frame rate)
Text: 32K SentencePiece vocabulary