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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+pplx-embed-v1-0.6b/tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,8 @@
+# AI-PC Benchmarks Embedding Intel OpenVINO
+
+Contains the runnable `pplx-embed-v1-0.6b` tokenizer files and static 1x512 OpenVINO export used by the Intel embedding benchmark.
+
+Layout:
+- `pplx-embed-v1-0.6b/tokenizer.json`
+- `pplx-embed-v1-0.6b/openvino/pplx-embed-v1-0.6b-static-1x512/model.xml`
+- `pplx-embed-v1-0.6b/openvino/pplx-embed-v1-0.6b-static-1x512/model.bin`

pplx-embed-v1-0.6b/1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "word_embedding_dimension": 1024,
+    "pooling_mode_cls_token": false,
+    "pooling_mode_mean_tokens": true,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false,
+    "pooling_mode_weightedmean_tokens": false,
+    "pooling_mode_lasttoken": false,
+    "include_prompt": true
+}

pplx-embed-v1-0.6b/README.md

@@ -0,0 +1,191 @@
+---
+license: mit
+pipeline_tag: feature-extraction
+tags:
+- feature-extraction
+- sentence-similarity
+- mteb
+- sentence-transformers
+language:
+- multilingual
+---
+
+
+<p align="center">
+  <img src="assets/logo.svg" alt="Perplexity Logo" width="400">
+</p>
+
+<p align="center">pplx-embed-v1: Diffusion-Pretrained Dense and Contextual Embeddings</p>
+
+`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*.
+
+
+![diag.png](assets/diag.png)
+
+## Models
+
+| Model | Dimensions | Context | MRL | Quantization | Instruction | Pooling |
+|:-----:|:----------:|:-------:|:---:|:------------:|:-----------:|:-------:|
+| `pplx-embed-v1-0.6B` | 1024 | 32K | Yes | INT8/BINARY | No | Mean |
+| `pplx-embed-v1-4B` | 2560 | 32K | Yes | INT8/BINARY | No | Mean |
+| `pplx-embed-context-v1-0.6B` | 1024 | 32K | Yes | INT8/BINARY | No | Mean |
+| `pplx-embed-context-v1-4B` | 2560 | 32K | Yes | INT8/BINARY | No | Mean |
+
+<sub>All models are built on diffusion continued pre-trained Qwen3 at Perplexity AI.</sub>
+
+<sub>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.</sub>
+
+## Usage
+
+<details>
+<summary>Via API</summary>
+
+```bash
+curl -X POST https://api.perplexity.ai/v1/embeddings \
+  -H "Authorization: Bearer YOUR_API_KEY" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "input": [
+      "Scientists explore the universe driven by curiosity.",
+      "Children learn through curious exploration.",
+      "Historical discoveries began with curious questions.",
+      "Animals use curiosity to adapt and survive.",
+      "Philosophy examines the nature of curiosity."
+    ],
+    "model": "pplx-embed-v1-0.6b"
+  }'
+```
+
+</details>
+
+
+<details>
+<summary>Using SentenceTransformers</summary>
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer(
+    "perplexity-ai/pplx-embed-v1-0.6B",
+    trust_remote_code=True
+)
+
+texts = [
+    "Scientists explore the universe driven by curiosity.",
+    "Children learn through curious exploration.",
+    "Historical discoveries began with curious questions.",
+    "Animals use curiosity to adapt and survive.",
+    "Philosophy examines the nature of curiosity.",
+]
+
+embeddings = model.encode(texts) # Shape: (5, 1024), quantized to int8
+embeddings = model.encode(texts, quantization="binary") # Shape: (5, 1024), quantized to binary
+```
+
+</details>
+
+<details>
+<summary> Using ONNX models </summary>
+
+```python
+
+import onnxruntime as ort
+from transformers import AutoTokenizer
+import numpy as np
+
+tokenizer = AutoTokenizer.from_pretrained("perplexity-ai/pplx-embed-v1-0.6b", trust_remote_code=True)
+session = ort.InferenceSession("onnx/model.onnx")
+
+
+texts = [
+    "Scientists explore the universe driven by curiosity.",
+    "Children learn through curious exploration.",
+    "Historical discoveries began with curious questions.",
+    "Animals use curiosity to adapt and survive.",
+    "Philosophy examines the nature of curiosity.",
+]
+
+tokenized = tokenizer(
+    texts,
+    padding=True,
+    truncation=True,
+    return_tensors="np"
+)
+
+onnx_inputs = {
+    "input_ids": tokenized["input_ids"].astype(np.int64),
+    "attention_mask": tokenized["attention_mask"].astype(np.int64),
+}
+
+# Run inference
+onnx_embeddings = session.run([out.name for out in session.get_outputs()], onnx_inputs)
+
+# ONNX produces both int8 and binary precision embeddings:
+int8_embeddings = onnx_embeddings[2]
+binary_embeddings = onnx_embeddings[3]
+packed_embeddings = np.packbits(binary_embeddings != -1, axis=-1)
+```
+
+</details>
+
+<details>
+<summary>Using Text Embeddings Inference (TEI)</summary>
+
+> [!NOTE]
+> Text Embeddings Inference v1.9.2+ is required.
+
+> [!IMPORTANT]
+> Currently, only int8-quantized embeddings are available via TEI. Remember to use cosine similarity with unnormalized int8 embeddings.
+
+- CPU w/ Candle:
+
+```bash
+docker run -p 8080:80 ghcr.io/huggingface/text-embeddings-inference:cpu-1.9 --model-id perplexity-ai/pplx-embed-v1-0.6B --dtype float32
+```
+
+- CPU w/ ORT (ONNX Runtime):
+
+```bash
+docker run -p 8080:80 ghcr.io/huggingface/text-embeddings-inference:cpu-1.9 --model-id onnx-community/pplx-embed-v1-0.6B --dtype float32
+```
+
+- GPU w/ CUDA:
+
+```bash
+docker run --gpus all --shm-size 1g -p 8080:80 ghcr.io/huggingface/text-embeddings-inference:cuda-1.9 --model-id perplexity-ai/pplx-embed-v1-0.6B --dtype float32
+```
+
+> If you hit OOM during warmup, lower --max-batch-tokens and --max-client-batch-size. Set --max-batch-tokens to max_sequence_length × batch_size (e.g., 2048 tokens × 8 sequences = 16384).
+
+> Alternatively, when running in CUDA you can use the architecture / compute capability specific
+> container instead of the `cuda-1.9`, as that includes the binaries for Turing, Ampere, Hopper and
+> Blackwell, so using a dedicated container will be lighter e.g., `ampere-1.9`.
+
+And then you can send requests to it via cURL to `/embed`:
+
+```bash
+curl http://0.0.0.0:8080/embed \
+  -H "Content-Type: application/json" \
+  -d '{
+    "inputs": [
+      "Scientists explore the universe driven by curiosity.",
+      "Children learn through curious exploration.",
+      "Historical discoveries began with curious questions.",
+      "Animals use curiosity to adapt and survive.",
+      "Philosophy examines the nature of curiosity."
+    ],
+    "normalize": false
+  }'
+```
+</details>
+
+
+## Technical Details
+
+For comprehensive technical details and evaluation results, see our paper on arXiv: https://arxiv.org/abs/2602.11151.

pplx-embed-v1-0.6b/added_tokens.json

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+{
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+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

pplx-embed-v1-0.6b/config.json

@@ -0,0 +1,76 @@
+{
+  "architectures": [
+    "PPLXQwen3Model"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration.PPLXQwen3Config",
+    "AutoModel": "modeling.PPLXQwen3Model"
+  },
+  "bos_token_id": 151643,
+  "dtype": "float32",
+  "eos_token_id": 151643,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "bidirectional_pplx_qwen3",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000,
+    "rope_type": "default"
+  },
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.0.0.dev0",
+  "transformers.js_config": {
+    "use_external_data_format": {
+      "model.onnx": 2,
+      "model_quantized.onnx": 1,
+      "model_q4.onnx": 1
+    }
+  },
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936,
+  "attn_implementation": "sdpa",
+  "use_bidirectional_attention": true
+}

pplx-embed-v1-0.6b/modules.json

@@ -0,0 +1,21 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "",
+    "type": "st_quantize.FlexibleQuantizer",
+    "kwargs": ["quantization"]
+  }
+]

pplx-embed-v1-0.6b/openvino/pplx-embed-v1-0.6b-static-1x512/export_metadata.json

@@ -0,0 +1,24 @@
+{
+  "model_dir": "D:\\OpenVino_Convert\\pplx-embed-v1-0.6b",
+  "output_xml": "D:\\OpenVino_Convert\\pplx-embed-v1-0.6b\\openvino\\pplx-embed-v1-0.6b-static-1x512\\model.xml",
+  "output_bin": "D:\\OpenVino_Convert\\pplx-embed-v1-0.6b\\openvino\\pplx-embed-v1-0.6b-static-1x512\\model.bin",
+  "staging_onnx": "D:\\OpenVino_Convert\\pplx-embed-v1-0.6b\\openvino\\pplx-embed-v1-0.6b-static-1x512\\model.standard.onnx",
+  "source_note": "Direct conversion from onnx/model_quantized.onnx is blocked by ONNX Runtime custom ops, so this IR is exported from local PyTorch weights and validated against the quantized ONNX outputs.",
+  "static_shape": {
+    "input_ids": [
+      1,
+      512
+    ],
+    "attention_mask": [
+      1,
+      512
+    ]
+  },
+  "input_dtype": "int32",
+  "output_names": [
+    "last_hidden_state",
+    "pooler_output",
+    "pooler_output_int8",
+    "pooler_output_binary"
+  ]
+}

pplx-embed-v1-0.6b/openvino/pplx-embed-v1-0.6b-static-1x512/model.bin

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+size 2384732488

pplx-embed-v1-0.6b/openvino/pplx-embed-v1-0.6b-static-1x512/validation_report.json

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+{
+  "ir_xml": "D:\\OpenVino_Convert\\pplx-embed-v1-0.6b\\openvino\\pplx-embed-v1-0.6b-static-1x512\\model.xml",
+  "onnx_path": "D:\\OpenVino_Convert\\pplx-embed-v1-0.6b\\onnx\\model_quantized.onnx",
+  "available_devices": [
+    "CPU",
+    "GPU",
+    "NPU"
+  ],
+  "validated_devices": {
+    "CPU": {
+      "status": "ok",
+      "compile_config": {},
+      "max_pooler_abs_diff": 0.026273906230926514,
+      "max_pooler_mean_abs_diff": 0.0028607603162527084,
+      "min_pooler_cosine_similarity": 0.9996875178276693,
+      "min_int8_match_ratio": 0.638671875,
+      "min_binary_match_ratio": 0.9921875,
+      "per_text": [
+        {
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+          "pooler_mean_abs_diff": 0.0028607603162527084,
+          "pooler_cosine_similarity": 0.999762744326066,
+          "int8_match_ratio": 0.646484375,
+          "binary_match_ratio": 0.9931640625,
+          "text": "OpenVINO runs the embedding model at a fixed 512 token context."
+        },
+        {
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+          "text": "CPU, GPU, and NPU parity matters more than nominal compilation."
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+          "int8_match_ratio": 0.6826171875,
+          "binary_match_ratio": 0.9921875,
+          "text": "This validation checks the quantized ONNX reference against the exported IR."
+        }
+      ]
+    },
+    "GPU": {
+      "status": "ok",
+      "compile_config": {
+        "INFERENCE_PRECISION_HINT": "f32"
+      },
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+          "int8_match_ratio": 0.646484375,
+          "binary_match_ratio": 0.9931640625,
+          "text": "OpenVINO runs the embedding model at a fixed 512 token context."
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+          "binary_match_ratio": 0.994140625,
+          "text": "CPU, GPU, and NPU parity matters more than nominal compilation."
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+          "text": "This validation checks the quantized ONNX reference against the exported IR."
+        }
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+      "status": "ok",
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+          "text": "OpenVINO runs the embedding model at a fixed 512 token context."
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+          "pooler_mean_abs_diff": 0.002866966649889946,
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+          "binary_match_ratio": 0.9931640625,
+          "text": "CPU, GPU, and NPU parity matters more than nominal compilation."
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+          "int8_match_ratio": 0.431640625,
+          "binary_match_ratio": 0.990234375,
+          "text": "This validation checks the quantized ONNX reference against the exported IR."
+        }
+      ]
+    }
+  }
+}

pplx-embed-v1-0.6b/special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
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+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "â½Ĺ",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

pplx-embed-v1-0.6b/tokenizer.json

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+size 11422837

pplx-embed-v1-0.6b/tokenizer_config.json

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+{
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+  "add_prefix_space": false,
+  "added_tokens_decoder": {
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