DUSUNEN-Pusula-118M-v0

DUSUNEN Pusula 118M v0

A 117.7M-parameter, 384-dimensional Turkish retrieval experiment built to measure the quality, index-memory and browser-deployment trade-off of a smaller encoder. It is a retriever, not a chat or text-generation model.

Five-task measured results

Official MTEB 2.18.16 evaluators, Turkish-only subsets, normalized embeddings and explicit prompt formats were used for every row.

Relative to DUSUNEN Rota 270M v1, this model uses 56.1% fewer parameters and 40% smaller float32 vectors, with a 15.2% relative macro-score reduction. The source-domain triplet accuracy rose from 0.8745 to 0.9200. This is a measured size/quality option, not a universal quality replacement.

Use

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("GoktugD/DUSUNEN-Pusula-118M-v0")
query = model.encode("Hard negative neden önemlidir?", normalize_embeddings=True)
docs = model.encode(
    ["Zor negatifler karar sınırını güçlendirir.", "Ankara Türkiye'nin başkentidir."],
    normalize_embeddings=True,
)
print(docs @ query)

Queries and documents are encoded as plain text. Store normalized vectors and use cosine similarity or inner product.

Training and deployment

• Pinned base revision: e8f8c211226b894fcb81acc59f3b34ba3efd5f42
• Data: 100,000 train / 2,000 validation triplets
• Objective: Cached Multiple Negatives Ranking Loss
• Sequence length: 256; effective batch: 64; BF16; seed: 3407
• Hardware: one RTX 5060 Laptop GPU with 8 GB VRAM
• Training time: 1,397.5 seconds; peak allocated GPU memory: 1.073 GB

The included dynamic-int8 ONNX graph is 118,335,516 bytes. On 16 documents and four probes, PyTorch-to-INT8 mean cosine agreement was 0.990002, minimum agreement was 0.977650 and top-1 agreement was 100%.

Limitations

• The training data is machine-translated Turkish MS MARCO and inherits its translation artifacts, dated facts and web-text biases.
• Compactness reduces retrieval quality on this five-task suite.
• Similarity is a ranking signal, not a calibrated probability or fact check.
• Exact-overlap auditing does not detect paraphrases or semantic duplicates.

The release includes raw MTEB results, training state, environment metadata, ONNX validation and SHA-256 file manifests.