Initial export with WAMP attention weights (INT8 ONNX)

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model.onnx.data filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+library_name: setfit
+license: mit
+base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+tags:
+- setfit
+- onnx
+- attention-weights
+- context-compression
+- intent-classification
+- multilingual
+pipeline_tag: text-classification
+---
+
+# SetFit Multilingual OVR Router (ONNX with Attentions)
+
+This is a State-of-the-Art **SetFit** model exported to **ONNX** format, specifically trained to classify LLM tasks into three semantic categories: **Needle** (Fact Retrieval), **Reasoning** (Logic/Analysis), and **Summary** (General Recap).
+
+The model is based on [paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) and has been modified to expose **all 12 layers of raw attention weights**.
+
+## Key Features
+
+- **3-Class Classification:** High-precision separation of intents.
+- **Multilingual:** Native support for Russian, English, and 50+ other languages.
+- **Attention Output:** Every inference returns a full attention matrix `(batch, heads, seq_len, seq_len)` for all 12 layers.
+- **Dual Precision:** Both **FP32** (`model.onnx`) and **INT8 Quantized** (`model_quantized.onnx`) versions are available.
+- **Optimized for CPU:** Fast ONNX inference via `onnxruntime`.
+
+## Classification Map
+- **Label 0:** Summary (Chatter, Recaps, TL;DR)
+- **Label 1:** Needle (Pinpoint facts, parameters, keys, IPs)
+- **Label 2:** Reasoning (Comparison, analysis, code debugging, logical chains)
+
+## Project Origin
+
+This model is a core component of the **[WAMP-proxy](https://github.com/naranor/wamp-proxy)** project, an intelligent middleware for research into LLM context optimization.
+
+## Quick Inference (Python)
+
+```python
+import numpy as np
+import onnxruntime as ort
+from transformers import AutoTokenizer
+import json
+
+# 1. Load model and weights
+session = ort.InferenceSession("model.onnx")
+tokenizer = AutoTokenizer.from_pretrained(".")
+with open("router_weights_setfit.json", "r") as f:
+    weights = json.load(f)
+
+# 2. Prepare Input
+text = "What is the database port?"
+inputs = tokenizer(text, return_tensors="np")
+onnx_inputs = {
+    "input_ids": inputs["input_ids"].astype(np.int64),
+    "attention_mask": inputs["attention_mask"].astype(np.int64)
+}
+
+# 3. Run
+outputs = session.run(None, onnx_inputs)
+embeddings = np.mean(outputs[0], axis=1) # Mean pooling
+
+# 4. Predict probabilities (LogReg Head)
+scores = np.dot(embeddings, np.array(weights["coef"]).T) + weights["intercept"]
+probs = np.exp(scores) / np.exp(scores).sum()
+print(f"Probabilities: {probs}")
+```
+
+## License
+MIT License.

model.onnx

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+size 1229334

model.onnx.data

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model_quantized.onnx

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router_weights_setfit.json

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special_tokens_map.json

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+{
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+}

tokenizer.json

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+oid sha256:3a56def25aa40facc030ea8b0b87f3688e4b3c39eb8b45d5702b3a1300fe2a20
+size 17082734

tokenizer_config.json

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+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "max_length": 128,
+  "model_max_length": 128,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}