Qwen/BlenderWorld
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Papers
Last Secure Code Benchmark: Is the Code You Vibe Secure?
VisionWeave: Weaving Elastic Visual Representations as a Native Capability of MLLMs
VoyagerXHF
published a
dataset 4 days ago
VoyagerXHF
updated a
dataset 4 days ago
wutianyi86
submitted a
paper to Daily Papers 5 days ago
Add Agents' Last Exam eval results (24.3 pass rate / 51.2 score, from model card)
#74 opened 7 days ago
by 
SaylorTwift
naykun
updated a
model 7 days ago
yuanfengustc
submitted a
paper to Daily Papers 8 days ago
gradio workflow app
#2 opened 10 days ago
by 
akhaliq
naykun
published a
model 10 days ago
Zhenru
authored 3
papers 10 days ago
RecreationWorld: Scalable and Verifiable Environments for Hybrid Computer-Use Agents
Paper • 2609.22000 • Published • 81
Terminal-Universe: Turning Agent Trajectories into Scalable Terminal Environments
Paper • 2609.04148 • Published • 134
Qwen-CUA: Native Computer Use for (almost) Everything
Paper • 2608.02352 • Published • 2
Update README.md
#1 opened 10 days ago
by 
kelseye
Update README.md
#1 opened 10 days ago
by kelseye
danielhanchen
posted an update 11 days ago
Post
4,948
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
- 5 replies
tuidan
submitted a
paper to Daily Papers 13 days ago
danielhanchen
posted an update 13 days ago
Post
4,032
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
submitted a
paper to Daily Papers 13 days ago
naykun
updated a
model 20 days ago
chujiezheng
authored 2
papers 20 days ago