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🤗 Working on GRM Models

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DedeProGames
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AI & ML interests

Thinking and Agentic Finetuning

Recent Activity

liked a Space about 4 hours ago
vineeth98/lora-speedrun
reacted to vineeth98's post with 👍 about 4 hours ago
I made a speedrun leaderboard for LoRA fine-tuning. One frozen task (Qwen2.5-1.5B to 57% on GSM8K), one GPU, fastest training run wins. Every record gets re-run 3x with fresh seeds on identical hardware before it counts, so no self-reported numbers. The baseline was 11:57 three days ago. Someone already got it down to 1:44, with data pruning and a chunked cross-entropy that never materializes the logits. Attempting is free (Modal's monthly credits cover full runs), and the second track (SmolLM2 + SQuAD) is still sitting at its naive baseline — easy first record for someone. https://huggingface.co/spaces/vineeth98/lora-speedrun
reacted to Leon5201314's post with 🔥 about 4 hours ago
0.7B MonkeyOCRv2 Outperforms Larger Models on 17-Language Document Parsing MonkeyOCRv2-B-Parsing reaches 83.3 on MDPBench, a multilingual benchmark covering digital-born and photographed documents across 17 languages. Results among evaluated open-source models: • MonkeyOCRv2-B, 0.7B: 83.3 • dots.mocr, 3B: 80.5 • HunyuanOCR-1.5, 1B: 76.8 • PaddleOCR-VL-1.6, 0.9B: 75.0 • MinerU2.5-Pro, 1.2B: 71.0 The central idea is simple: before asking an LLM to reason over a document, the vision encoder must preserve every character stroke, digit, punctuation mark, and layout cue. MonkeyOCRv2 is pretrained on 113M document images across 17 languages using joint image-to-text generation and pixel-level reconstruction. Models: https://huggingface.co/collections/zenosai/monkeyocrv2 Paper: https://huggingface.co/papers/2607.11562 GitHub: https://github.com/Yuliang-Liu/MonkeyOCRv2 Code and model weights are available under Apache-2.0. We welcome tests on difficult multilingual, photographed, and visually ambiguous documents—especially failure cases.
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