sumo43

Try running this using their harness https://huggingface.co/flashresearch/FlashResearch-4B-Thinki...

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

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

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 generalizing on OOD.

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 Engineer & Research Scientist. Ex AI grant startup, hedge fund. I've previously worked on inference for LLMs and vision models & have experience with data curation and multinode training. Looking for summer internships or part time positions

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.

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 included.

email tyoma9k@gmail.com

edit: formatting

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 parts of the country.

Here I'm assuming that Petal uses a large number of small, heterogenous nodes like consumer gpus. It might as well be something much simpler.

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 significant latency from parallelism schemes across different servers.

p.s. from experience instruct-finetuning falcon180b, it's not worth using over llama2-70b as it's significantly undertrained.

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