jamie8johnson/bge-large-v1.5-code-search

BGE-large Code Search (LoRA fine-tuned)

A fine-tuned code search embedding model based on BAAI/bge-large-en-v1.5 (335M parameters, 1024 dimensions). Trained with call-graph false-negative filtering on 200K balanced pairs across 9 programming languages. Built for cqs — code intelligence and RAG for AI agents.

Key Results

Eval	Metric	This Model	BGE-large Baseline	v9-200k (110M)
Fixture (296q, 7 languages, enriched)	R@1	91.6%	90.9%	90.5%
Fixture	MRR	0.952	0.949	0.948
Raw code embedding (55q, no enrichment)	R@1	66.2%	61.8%	70.9%
Real codebase (100q lookup)	R@1	50.0%	50.0%	26.0%
Real codebase	R@5	73.0%	72.0%	51.0%
CoIR 9-task (19 subtasks)	Overall	57.5	55.7	52.7
CoIR CodeSearchNet (6 languages)	NDCG@10	0.779	0.721	0.615

New best on every metric except raw R@1 (where v9-200k's 70.9% still leads). Fine-tuning is additive with model capacity — BGE-large did not basin like E5-base variants.

Training Details

• Base Model: BAAI/bge-large-en-v1.5 (335M params, 1024 dimensions)
• Data: 200K balanced pairs (22,222 per language × 9 languages) from cqs-indexed Stack repos
• Key Technique: Call-graph false-negative filtering — excludes structurally related functions from contrastive negatives
• Loss: CachedGISTEmbedLoss (guide: intfloat/e5-base-v2, margin 0.05) + MatryoshkaLoss (1024/512/256/128 dims)
• LoRA: rank 16, alpha 32, dropout 0.1 (targets: query, key, value, dense)
• Epochs: 1 (5938 steps, batch size 32)
• Hardware: NVIDIA RTX A6000 (48GB), ~12.75 hours
• Final loss: train 0.161, eval 0.068
• Dataset: jamie8johnson/cqs-code-search-200k

Enrichment Ablation

Fine-tuning slightly increases enrichment dependency vs baseline:

Layer Skipped	R@1	Delta	Baseline Delta
None (full)	91.6%	—	—
doc	84.1%	-7.5pp	-6.8pp
filecontext	86.8%	-4.8pp	-4.1pp
signatures	89.9%	-1.7pp	-1.4pp
callgraph	90.9%	-0.7pp	-0.4pp

Supported Languages

Go, Java, JavaScript, PHP, Python, Ruby, Rust, TypeScript, C++

Usage

With cqs

# Download and use as custom model
export CQS_ONNX_DIR=/path/to/this/model/onnx
export CQS_EMBEDDING_DIM=1024
cqs index --force

With sentence-transformers

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("jamie8johnson/bge-large-v1.5-code-search")
query_emb = model.encode("Represent this sentence for searching relevant passages: find functions that validate email addresses")
code_emb = model.encode("def validate_email(addr): ...")

Note: BGE-large uses an instruction prefix for queries: "Represent this sentence for searching relevant passages: ". Passages have no prefix.

Files

• merged_model/ — full merged weights (sentence-transformers compatible)
• lora_adapter/ — LoRA adapter only (for PEFT)
• onnx/model.onnx — ONNX format for cqs/ORT inference (1.27GB, opset 11)
• onnx/tokenizer.json — tokenizer for ONNX inference

License

Apache 2.0 (same as base model)