Roo GGUF (Q4_K_M + BF16) + reference + cross-vendor card

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
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+moss-tts-local-roo-ip172-BF16.gguf filter=lfs diff=lfs merge=lfs -text
+moss-tts-local-roo-ip172-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+reference_24k.wav filter=lfs diff=lfs merge=lfs -text

README.md

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 ---
 license: apache-2.0
+base_model: OpenMOSS-Team/MOSS-TTS-Local-Transformer
+pipeline_tag: text-to-speech
+tags:
+  - text-to-speech
+  - tts
+  - moss-tts
+  - gguf
+  - llama.cpp
+  - q4_k_m
+  - reference-conditioned
+language:
+  - en
 ---
+
+![Roo Voice](https://design8.b-cdn.net/huggingface/roo-voice-transformers.png)
+
+# Roo-Voice · MOSS-TTS-Local-Transformer · GGUF (llama.cpp)
+
+Roo's voice — that signature baritone with the estuary accent that stands the hair up on the back of
+your neck — as a **GGUF** model for **llama.cpp**. This is the **cross-vendor** build: one file that
+runs on **AMD, NVIDIA, and CPU** (Vulkan / ROCm / CUDA / Metal), because llama.cpp is portable across
+all of them. Load it, and Roo can whisper to you all day long.
+
+A GGUF conversion of a **full-model supervised fine-tune** of
+[MOSS-TTS-Local-Transformer](https://huggingface.co/OpenMOSS-Team/MOSS-TTS-Local-Transformer),
+trained on Roo's own recordings and specialised on his single voice.
+
+## ⚠️ What actually makes the voice — read first
+
+This is a **reference-conditioned** model, and both halves matter:
+
+- **The fine-tune is what makes it Roo.** The base model has never heard this speaker — a base model
+  plus any reference clip will *not* give you Roo's baritone or his estuary accent. That voice lives
+  in the weights, put there by the supervised fine-tune on his recordings.
+- **The reference completes the delivery.** `reference_24k.wav` (bundled) conditions the fine-tuned
+  model at inference and is **required** to produce the voice.
+
+## Files
+
+| File | What | Size |
+|---|---|---|
+| `moss-tts-local-roo-ip172-Q4_K_M.gguf` | **4-bit** (recommended) — smallest, GPU-friendly | ~1.76 GB |
+| `moss-tts-local-roo-ip172-BF16.gguf` | Full-precision GGUF (largest quality headroom) | ~5.5 GB |
+| `reference_24k.wav` | The identity reference (**required**) | — |
+
+## Why GGUF here — the cross-vendor lane
+
+The same portable model runs on both GPU vendors; only a couple of formats are locked to one:
+
+| Build | AMD | NVIDIA | Apple |
+|---|---|---|---|
+| **This GGUF** (llama.cpp) | ✅ Vulkan / ROCm | ✅ CUDA / Vulkan | ✅ Metal |
+| [MLX 4-bit / 8-bit](https://huggingface.co/abliter8-ai/Roo-Voice_MOSS_TTS_LT_mlx4) | — | — | ✅ only |
+| [int4 / int8](https://huggingface.co/abliter8-ai/Roo-Voice_MOSS_TTS_LT_int4) (bitsandbytes) | ⚠️ experimental ROCm | ✅ | — |
+
+**If you're on AMD Radeon, this GGUF is your path** — `bitsandbytes` (the int4/int8 builds) is
+NVIDIA-first and unreliable on consumer Radeon, so use llama.cpp's Vulkan or ROCm backend here instead.
+
+> **AMD support is experimental / best-effort.** This build is validated on **NVIDIA CUDA**. On AMD,
+> a **desktop-class Radeon (RDNA3/4 dGPU) with ROCm** is expected to accelerate well, but we could not
+> fully validate it on our own hardware. Note: on a low-power **iGPU** (e.g. Radeon 890M) the current
+> MOSS-TTS llama.cpp fork's custom ops did **not** offload to the GPU under Vulkan in our testing and
+> ran CPU-bound — so treat iGPU performance as CPU-class for now. Contributions of Vulkan/ROCm kernels
+> for the MOSS-TTS ops would improve this. For AMD, **SGLang-Omni / vLLM-Omni (ROCm)** are also worth
+> trying for the transformers builds.
+
+## How to run it
+
+MOSS-TTS Local is a two-part system: an **LM backbone** (this GGUF, run by llama.cpp) and a neural
+**audio codec** (the MOSS-Audio-Tokenizer, run via ONNX Runtime) that turns the model's tokens into
+24 kHz audio and encodes the reference. You need the OpenMOSS llama.cpp integration, which adds the
+`llama-moss-tts` tool and the delay-pattern / 32-codebook decoding:
+
+1. **Build the fork** — OpenMOSS's MOSS-TTS llama.cpp integration
+   ([PR](https://github.com/OpenMOSS/llama.cpp/pull/6)). Build with your GPU backend:
+   `-DGGML_VULKAN=ON` (portable, best on AMD), `-DGGML_CUDA=ON` (NVIDIA), or `-DGGML_HIP=ON` (AMD ROCm).
+2. **Get the codec** — `OpenMOSS-Team/MOSS-Audio-Tokenizer` (Apache-2.0). Export or fetch its ONNX
+   encoder/decoder for ONNX Runtime. Install `onnxruntime` (CPU) or the GPU package matching your
+   CUDA/ROCm for fast decode.
+3. **Build the generation reference** from `reference_24k.wav` + your text
+   (`tools/tts/moss-tts-build-generation-ref.py`), then run `llama-moss-tts` with `--n-gpu-layers -1`
+   and point `--audio-decoder-onnx` at the codec. It emits a 24 kHz WAV.
+
+> **Note on speed:** put the codec on the GPU (an onnxruntime GPU EP, or DirectML on Windows/AMD). The
+> LM backbone is fast on GPU; if the *codec* decode runs on CPU it becomes the bottleneck.
+
+Decoding contract for this voice: seed 42, temperature 1.0, top-k 50, top-p 0.95, repetition
+penalty 1.1, 32 RVQ codebooks, 24 kHz.
+
+## Prefer a turnkey path?
+
+If you don't want to build the fork: on **Apple Silicon** use the
+[MLX builds](https://huggingface.co/abliter8-ai/Roo-Voice_MOSS_TTS_LT_mlx4); on **NVIDIA** use the
+[int4 / int8](https://huggingface.co/abliter8-ai/Roo-Voice_MOSS_TTS_LT_int4) transformers builds. The
+GGUF is the one to reach for when you want **AMD** or a single cross-vendor file.
+
+## Limitations
+
+- **Reference-conditioned** — the bundled `reference_24k.wav` must ride along; there is no text-only path.
+- Single voice by design (this is Roo, not a multi-speaker system).
+- Requires the OpenMOSS MOSS-TTS llama.cpp integration + the ONNX audio codec — it is not a
+  drop-in `llama-cli` model.
+
+## Provenance & license
+
+Quantized/exported form of an accepted single-speaker MOSS-TTS Local supervised fine-tune. The base
+model and audio codec are Apache-2.0 (OpenMOSS); weights derived from them are redistributed here
+under the same license.

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