sumo43

5 ms·

1.
by sumo43 11mo ago
Try running this using their harness https://huggingface.co/flashresearch/FlashResearch-4B-Thinki...
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by sumo43 11mo ago
I made a 4B Qwen3 distill of this model (and a synthetic dataset created with it) a while back. Both can be found here: https://huggingface.co/flashresearch
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4B parameter Deep Research model based on Qwen(huggingface.co)
1 points by sumo43 11mo ago|0 comments
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by sumo43 2y ago
I think the fine tuned policies are still very brittle, but I agree that this is super promising. It's also one of the most open (the model is still closed) research blogposts we've seen from any private embodied AI lab
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Try out AI robotics models in your browser(robotarena.ai)
2 points by sumo43 2y ago|0 comments
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by sumo43 2y ago
seems like an improvement on the aloha approach? You still need to finetune it on roughly the same amount of OOD examples. Contrast this with google's approach over 2023, which was training large vision-language models with the goal of
7.
by sumo43 2y ago
Location: US Remote: Yes Willing to relocate: Yes (US) Technologies: Python, PyTorch, HuggingFace, C++ Résumé/CV: https://drive.google.com/file/d/1qY-m1tKz4_QpHgxaGryC2vk-DGs... Email: sumo43@proton.me ML Eng
8.
Kolmogorov-Arnold Networks(github.com)
568 points by sumo43 2y ago|142 comments
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by sumo43 2y ago
Maybe true for instruct, but pretraining datasets do not usually contain GPT-4 outputs. So the base model does not rely on GPT-4 in any way.
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by sumo43 3y ago
SEEKING VOLUNTEERS: open source self-play training for language models we are a small team associated with EleutherAI. looking to push the frontier of open source language models through self-play. so far we have implemented SPIN. compute i
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by sumo43 3y ago
For training you would need more memory. As for the pooling, Theoretically yes but wouldn't latency play as much, if not a greater part in the response time here? Imagine a tensor-parallel gather where the other nodes are in different
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by sumo43 3y ago
Cool service. It's worth noting that, with quantization/QLORA, models as big as llama2-70b can be run on consumer hardware (2xRTX 3090) at acceptable speeds (~20t/s) using frameworks like llama.cpp. Doing this avoids the sign
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by sumo43 3y ago
Hello, I'm planning to participate in this challenge. I have experience training/prompting and building products from LLMs, I've also participated in a few CTFs. sumo43@proton.me