Hugging Face's logo
Burton Lancaster's picture
🐝 Article: Where the Hivemind Comes From

Burton Lancaster PRO

RiverRider
6 5 29
webbrain-one-773093's profile picture mm9244's profile picture Despoticc's profile picture
·
https://sunstonenorth.com

AI & ML interests

Explainable AI

Recent Activity

upvoted a collection 1 day ago
Motherlode
reacted to theirpost with 🤯 4 days ago
Motherlode on MTEB and Hugging Face Inference https://huggingface.co/RiverRider/motherlode-code-small-en-v0.1 is a 33.4 M-parameter English embedding model for code and text. It is a drop-in for bge-small-en-v1.5: the mean of two fine-tunes of that base, one trained to match a GitHub issue to the file that needs changing and one trained to reproduce the geometry of gte-modernbert-base (a 4.5× larger model). Where it differs on ranking metrics
On the MTEB(Code, v1) MultiSWEbenchRR task it leads the models under 100 M parameters at recall@10 (52.48 versus 52.18 for granite-embedding-small-english-r2). On the clean subset of SWE-bench Verified it improves recall@1 over the base model (178 of 411 versus 145), a gap that holds across the engine settings used in Sunstone. On the CoIR code-search probe it raises nDCG@10 from 0.467 to 0.576. The gains are concentrated on localization and code retrieval rather than uniform improvement across every MTEB task. The model remains competitive on the broader English MTEB(eng, v2) average (63.47, 4th of 15 under 100 M) while carrying a clear advantage on the code-file tasks it was built for. 
 The weights are compatible with Hugging Face Inference Providers for feature extraction and sentence similarity. The repository also ships matching ONNX, GGUF, and sentence-transformers exports. From Sunstone North Lab.
reacted to theirpost with 🔥 4 days ago
Motherlode on MTEB and Hugging Face Inference https://huggingface.co/RiverRider/motherlode-code-small-en-v0.1 is a 33.4 M-parameter English embedding model for code and text. It is a drop-in for bge-small-en-v1.5: the mean of two fine-tunes of that base, one trained to match a GitHub issue to the file that needs changing and one trained to reproduce the geometry of gte-modernbert-base (a 4.5× larger model). Where it differs on ranking metrics
On the MTEB(Code, v1) MultiSWEbenchRR task it leads the models under 100 M parameters at recall@10 (52.48 versus 52.18 for granite-embedding-small-english-r2). On the clean subset of SWE-bench Verified it improves recall@1 over the base model (178 of 411 versus 145), a gap that holds across the engine settings used in Sunstone. On the CoIR code-search probe it raises nDCG@10 from 0.467 to 0.576. The gains are concentrated on localization and code retrieval rather than uniform improvement across every MTEB task. The model remains competitive on the broader English MTEB(eng, v2) average (63.47, 4th of 15 under 100 M) while carrying a clear advantage on the code-file tasks it was built for. 
 The weights are compatible with Hugging Face Inference Providers for feature extraction and sentence similarity. The repository also ships matching ONNX, GGUF, and sentence-transformers exports. From Sunstone North Lab.
View all activity

Organizations

Blog-explorers's profile picture MLX Community's profile picture Hugging Face Discord Community's profile picture open/ acc's profile picture Sunstone North Lab's profile picture