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GGUF packaging of `perplexity-ai/PII-Tracer`, a PII masking model for conversational data. The model uses a ~600M-parameter bidirectional Qwen3 encoder with a `perplexity-ai/pplx-embed-v1-0.6b` backbone and two heads: The backbone is provided as an f16 GGUF for llama.cpp. Both heads remain in fp32 in `heads.safetensors`. The included Python client applies the heads to per-token hidden states from `llama-server`, decodes PII spans, and masks text. Install a compatible `llama-server` build and the Python client dependencies, then download the repository:

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MLX weights for PII-Tracer, a PII masking model for conversational data. The model uses a ~600M-parameter bidirectional Qwen3 encoder with a `perplexity-ai/pplx-embed-v1-0.6b` backbone and two heads: The backbone's linear projections use **8-bit affine quantization with group size 64**. Embeddings and normalization weights remain bf16; both heads remain fp32. The weights file is approximately 779 MB. Quantized projections retain the `backbone.*` key names and store packed `uint32` `weight` tensors with bf16 `scales` and `biases`. The unquantized heads use `token_cls_head.*` and `sensitivity_head.*`.

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llama.cpp GGUF build of the pplx-computer qwen3.8 27B checkpoint (`Qwen3_5ForConditionalGeneration`, dense hybrid GDN linear-attention + full attention every 4th of 64 layers), converted 2026-08-26 from the raw bf16 checkpoint. Ships **two speculative-decoding drafts — pick one**: the checkpoint's native MTP head, or the DFlash2 block-diffusion drafter. DFlash2 is **not** in llama.cpp master: build PR #27342 (`z-lab:dflash2`, verified at `f7aadef`). On master the draft fails with `done_getting_tensors: wrong number of tensors; expected 81, got 58`. DFlash2 drafts longer blocks per verification step (higher throughput ceiling); native MTP has a higher acceptance rate and runs on unpatched llama.cpp. Default to MTP unless you already run the PR build.

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`pplx-embed-v1` and `pplx-embed-context-v1` are state-of-the-art text embedding models optimized for real-world, web-scale retrieval tasks. - Use **`pplx-embed-v1`** for independent text embedding (queries, documents, semantic search) - Use **`pplx-embed-context-v1`** for document chunks in RAG systems where surrounding context matters > [!IMPORTANT] > `pplx-embed-v1` and `pplx-embed-context-v1` natively produce *unnormalized* int8-quantized embeddings. Ensure that you compare them via *cosine similarity*.

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`pplx-embed-v1` and `pplx-embed-context-v1` are state-of-the-art text embedding models optimized for real-world, web-scale retrieval tasks. > [!IMPORTANT] > `pplx-embed-v1` and `pplx-embed-context-v1` natively produce *unnormalized* int8-quantized embeddings. Ensure that you compare them via *cosine similarity*. Many modern embedding models rely on instruction tuning, where users prepend an instruction string to the text being embedded. This can yield a 2%-3% lift on benchmarks, but it also introduces prompt-selection overhead and can make indexing pipelines brittle (small instruction changes can shift embedding space). We deliberately **avoid** this requirement: you can embed the text you want to index directly, without having to choose or maintain an instruction prefix.

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