compiled artefacts from win_x64 (sm_120)

.gitattributes

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+code-daemon-reranker-v1_linux_x64_trt11.0_sm_120.engine filter=lfs diff=lfs merge=lfs -text
+code-daemon-reranker-v1_win_x64_trt11.0_sm_120.engine filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+license: mit
+language:
+- code
+- multilingual
+tags:
+- code
+- code-search
+- code-retrieval
+- reranker
+- cross-encoder
+- text-ranking
+- knowledge-distillation
+pipeline_tag: text-ranking
+base_model:
+- cross-encoder/mmarco-mMiniLMv2-L12-H384-v1
+datasets:
+- code_search_net
+- unicamp-dl/mmarco
+---
+
+# code-daemon-reranker-v1
+
+A small, fast **cross-encoder reranker** purpose-built to re-order first-stage code-search hits for
+precision. It ships with the [UltraCode](https://github.com/faxenoff/ultracode) MCP server as a
+TensorRT / OpenVINO / TVM engine, scoring **(query, candidate)** pairs after the embedding retriever
+has fetched a candidate pool.
+
+A reranker is the **second stage**: the bi-encoder embed model
+([`code-daemon-embed-v1`](https://huggingface.co/faxenoff/code-daemon-embed-v1)) retrieves a pool fast,
+then this cross-encoder reads each *(query, code)* pair **jointly** and emits a single relevance logit,
+pulling the best match to the top. Joint attention over the pair is far more precise than the cosine of
+two independent vectors — at the cost of one forward pass per candidate, so it scores only a bounded
+pool (~64), not the whole index.
+
+- **~117M params** — XLM-RoBERTa **12 layers / 384 hidden**, 250k multilingual SentencePiece vocab
+  (the embedding table dominates the size).
+- **2-input ONNX** (`input_ids`, `attention_mask`; no `token_type_ids`) → a single relevance **logit**.
+- **Max sequence 256** tokens for the concatenated *(query, document)* pair.
+- **Listwise-trained** — the key quality lever (below).
+
+## How it was made
+
+**Warm-started** from [`cross-encoder/mmarco-mMiniLMv2-L12-H384-v1`](https://huggingface.co/cross-encoder/mmarco-mMiniLMv2-L12-H384-v1)
+(a strong multilingual MS MARCO cross-encoder) and **fine-tuned with a listwise loss** — **ListNet
+top-1 softmax cross-entropy** over each query's *{1 positive + ≤8 hard negatives}* group. The hard
+negatives were mined by the code retriever
+[`nomic-ai/CodeRankEmbed`](https://huggingface.co/nomic-ai/CodeRankEmbed) from CoIR (NL→code,
+code-dominant 0.75 share): the documents the first stage confuses with the answer are exactly what the
+reranker must learn to push down. Trained on ~99k query groups, 2 epochs, on a single A100.
+
+**Why listwise (not pointwise BCE):** a listwise loss optimizes the *order of the whole candidate
+group*, not each pair in isolation, so it spends capacity on the **top of the list** — which is all a
+reranker is for. Against the same model trained with pointwise BCE, listwise lifted **Hit@1 +0.12 and
+MRR +0.10** on our golden set; recall (Hit@5/@10) is unchanged because that ceiling is set by the
+first-stage retriever, not the reranker.
+
+## Built for speed
+
+- **Short context (256)** — a *(query, code-unit)* pair is short; there is no long-document path.
+- **Bounded pool** — the daemon reranks only the top ~64 fused candidates (the relevant file often sits
+  at fused rank 30–60), then cuts back to top-k *after* reranking.
+- **Runs on the iGPU.** On a box with both NVIDIA + Intel, the daemon routes the reranker to the
+  **Intel iGPU (OpenVINO fp16)** and keeps CUDA free for the embedding model — the two search stages run
+  on different devices in parallel. TensorRT (NVIDIA) and TVM/Vulkan engines are bundled for boxes
+  without an Intel iGPU.
+- **fp16** — mmarco-format INT8 on the iGPU hits a known OpenVINO AccessViolation, so fp16 is shipped.
+
+## Intended use
+
+Re-rank a candidate pool from a first-stage retriever for **NL→code search** (multilingual text works
+too). Feed *(query, candidate)* pairs, take the logit, sort descending. The score is a **raw, unbounded
+logit** (Identity head) — compare relatively *within* a query, not against a fixed threshold.
+
+```python
+import onnxruntime as ort, numpy as np
+from transformers import AutoTokenizer
+
+tok  = AutoTokenizer.from_pretrained(".")            # bundled XLM-R SentencePiece
+sess = ort.InferenceSession("model.onnx", providers=["CPUExecutionProvider"])
+
+def rerank(query, docs, max_len=256):
+    enc = tok([query] * len(docs), docs, padding=True, truncation=True,
+              max_length=max_len, return_tensors="np", return_token_type_ids=False)
+    logits = sess.run(None, {"input_ids":      enc["input_ids"].astype(np.int64),
+                             "attention_mask": enc["attention_mask"].astype(np.int64)})[0]
+    return sorted(zip(logits.reshape(-1).tolist(), docs), reverse=True)   # higher = more relevant
+```
+
+## What's in this repo — ready-to-run compiled engines
+
+Named per **runtime × GPU arch × OS** (single-profile — no length buckets):
+
+- **TensorRT** `code-daemon-reranker-v1_{win_x64,linux_x64}_trt_sm_{86,89,120}.engine` — NVIDIA, fp16
+  (sm_86 ≈ RTX 30xx / A-series · sm_89 ≈ RTX 40xx / L4 · sm_120 ≈ RTX 50xx).
+- **OpenVINO** `code-daemon-reranker-v1_ov_{cpu,igpu}_fp16_b16_s256.{xml,bin}` — Intel CPU / iGPU.
+- **TVM** `code-daemon-reranker-v1_*_tvm_vulkan.{dll,so}` — Vulkan fallback for non-TRT / other GPUs.
+- **Tokenizer** — `sentencepiece.bpe.model` + `tokenizer_config.json` (XLM-R SP; the daemon loads it
+  directly).
+- **ONNX source** — `model.onnx` FP32 (the build source + standalone `onnxruntime` / `optimum` use).
+
+## Evaluation
+
+Measured on the daemon's own `search-gold` golden set (26 NL→code queries — its real query
+distribution), reranking the embed retriever's top-64 pool. Metrics are advisory; manual review of the
+failures is the source of truth.
+
+| metric | embed-only | **+ reranker** | Δ |
+|---|--:|--:|--:|
+| Hit@1 | 0.42 | **0.54** | **+0.12** |
+| Hit@3 | 0.62 | **0.77** | **+0.15** |
+| Hit@5 | 0.73 | **0.77** | +0.04 |
+| Hit@10 | 0.81 | 0.81 | 0 *(recall ceiling — set by the retriever)* |
+| MRR@10 | 0.55 | **0.65** | **+0.10** |
+| nDCG@10 | 0.59 | **0.67** | **+0.08** |
+
+The gains concentrate on the **top of the list** (Hit@1, MRR, nDCG) — the listwise signature. Hit@10 is
+flat because a reranker can only reorder what the retriever already fetched.
+
+## Performance
+
+| Backend | Hardware | Rerank latency |
+|---|---|--:|
+| OpenVINO fp16 | Intel iGPU (Xe) | **~550 ms / 64-candidate pool** (~8.6 ms/candidate) |
+
+The daemon runs the reranker on the iGPU so the NVIDIA GPU stays free for the embedding model; the
+TensorRT path is faster per pass where an NVIDIA GPU is used for reranking.
+
+## License & training data
+
+Released under the **MIT license** (the mmarco base + XLM-R backbone are MIT/Apache; fine-tuned weights
+released MIT). Training-data transparency:
+
+| Source | Note |
+|---|---|
+| `cross-encoder/mmarco-mMiniLMv2-L12-H384-v1` (warm-start base) | **mMARCO ← MS MARCO → non-commercial research terms** |
+| CoIR (NL→code hard-negative mining) | code-retrieval corpora; mixed upstream provenance |
+| hard-neg miner `nomic-ai/CodeRankEmbed` | MIT |
+
+⚠️ The warm-start base derives from **MS MARCO (non-commercial)**. Whether a fine-tuned model inherits
+dataset-use terms is legally unsettled; this is **not legal advice**. Retrain from a permissive base if
+strict compliance is required.
+
+## Attribution
+
+Warm-started from **[cross-encoder/mmarco-mMiniLMv2-L12-H384-v1](https://huggingface.co/cross-encoder/mmarco-mMiniLMv2-L12-H384-v1)**.
+Hard negatives mined with **[nomic-ai/CodeRankEmbed](https://huggingface.co/nomic-ai/CodeRankEmbed)** (MIT).
+Backbone: XLM-RoBERTa.

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manifest.json

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+  "model_id": "code-daemon-reranker-v1",
+  "dimension": 1,
+  "max_tokens": 256,
+  "quantization": "fp16",
+  "targets": [
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+    "mlx_dir": "model_gpu_mlx0.22"
+  },
+  "compiled_at": "2026-08-03T14:52:05Z",
+  "compiled_by": "models/_compile"
+}

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