VisionWeave: Weaving Elastic Visual Representations as a Native Capability of MLLMs
Paper • 2610.07987 • Published • 37
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VisionWeave: Weaving Elastic Visual Representations as a Native Capability of MLLMs
TRACE: Rollout-Guided Quantization-Aware Training for FP4 Reinforcement Learning of MoE Language Models
yuanfengustc
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naykun
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yuhao318
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VLCache: Computing 2% Vision Tokens and Reusing 98% for Vision-Language Inference
Paper • 2512.12977 • Published
Unified Low-rank Compression Framework for Click-through Rate Prediction
Paper • 2405.18146 • Published • 1
H-Scale: Hessian-Guided Scale Refinement for NVFP4 Sub-Byte LLM Inference
Paper • 2608.28113 • Published • 1
Effectively Compress KV Heads for LLM
Paper • 2406.07056 • Published • 1
QUADS: Stabilizing NVFP4 Reinforcement Learning for MoE via QUantization-error Alignment across Dual Sides
Paper • 2607.15810 • Published • 1
TRACE: Rollout-Guided Quantization-Aware Training for FP4 Reinforcement Learning of MoE Language Models
Paper • 2610.07767 • Published • 93
danielhanchen
posted an update 4 days ago
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4454
You can now train your own Decision model like Jev locally!
We increased Qwen3.5 0.8B’s aggregate accuracy from 20.7% to 74.3% across 3 decision benchmarks - on just 4GB VRAM.
Turn any LLM like Qwen3.8, Gemma 4 into decision models with our open-source Unsloth repo.
GitHub: https://github.com/unslothai/unsloth
Guide: https://unsloth.ai/docs/basics/train-your-own-decision-model-with-unsloth
We increased Qwen3.5 0.8B’s aggregate accuracy from 20.7% to 74.3% across 3 decision benchmarks - on just 4GB VRAM.
Turn any LLM like Qwen3.8, Gemma 4 into decision models with our open-source Unsloth repo.
GitHub: https://github.com/unslothai/unsloth
Guide: https://unsloth.ai/docs/basics/train-your-own-decision-model-with-unsloth
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tuidan
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paper to Daily Papers 5 days ago
danielhanchen
posted an update 6 days ago
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Google releases EmbeddingGemma 2, a new open embedding model that runs locally on 0.5GB RAM.
The 740M parameter Apache 2.0 model combines a 270M text model with vision (170M) + audio (300M).
Run & train the model via Unsloth.
GGUF: unsloth/embeddinggemma-2-GGUF
Guide: https://unsloth.ai/docs/models/embeddinggemma-2
The 740M parameter Apache 2.0 model combines a 270M text model with vision (170M) + audio (300M).
Run & train the model via Unsloth.
GGUF: unsloth/embeddinggemma-2-GGUF
Guide: https://unsloth.ai/docs/models/embeddinggemma-2
mjuicem
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paper to Daily Papers 6 days ago
alielfilali01
posted an update 11 days ago
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multi-agents orchestration is old news by now. Try multi-teams of agents ! that is a whole different nightmare ... This is the real signal about the sparks of AGI.
naykun
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model 12 days ago
chenmouxiang
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paper to Daily Papers 13 days ago
alibaba-qwen
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