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salma-remyx
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reacted to RiverRider's post with 🔥 about 2 hours 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 ifaposto's post with 🔥 2 days ago
Self-distillation methods usually rely on an Exponential Moving Average (EMA) teachers to get synced and stable critics. However, this doesn't scale well for large models since holding both teacher and student (trained) on GPU increases latency, zero3 communication, etc. We can mitigate this with LoRA finetuning: the backbone is shared & frozen, and we need to maintain two light adapters: one for the student (trained) and one for the teacher (EMA of the student adapter). Getting this to work cleanly under TRL and ZeRO-3 took some fixes, so I packaged it as a plug-and-play repo. It supports: -> robust SDFT implementation that runs under ZeRO-3 -> ZeRO-3 + LoRA integration with adapter switching for teacher/student passes -> Saving and resuming EMA teacher checkpoint -> working deepspeed configs that fix memory spikes when GPU memory is tight I tested on Qwen3.5 and ToolAlpaca. Repo: https://github.com/ifiaposto/sdft_zero3 Feedback/ more examples/ issues welcome!
reacted to africanspeech's post with 🔥 3 days ago
Introducing AfricanSpeech FR-CMR — Cameroon French Speech Dataset How well do ASR systems recognize spontaneous French spoken in Cameroon? We've released our dataset page featuring: - 1,172 speech clips | 162 speakers | 4.89 hours - Human-validated ASR references and demographic metadata - Audio demos and a benchmark comparing five ASR systems Available for commercial licensing and evaluation. Open to research and industry collaborations. Explore the dataset: https://huggingface.co/datasets/africanspeech/cameroon-french-speech Contact: contact@africanspeech.com
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