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"Reflection 70B holds its own against even the top closed-source models (Claude 3.5 Sonnet, GPT-4o).

It’s the top LLM in (at least) MMLU, MATH, IFEval, GSM8K.

Beats GPT-4o on every benchmark tested.

It clobbers Llama 3.1 405B. It’s not even close.

The technique that drives Reflection 70B is simple, but very powerful.

Current LLMs have a tendency to hallucinate, and can’t recognize when they do so.

Reflection-Tuning enables LLMs to recognize their mistakes, and then correct them before committing to an answer.

Additionally, we separate planning into a separate step, improving CoT potency and keeping the outputs simple and concise for end users.

Important to note: We have checked for decontamination against all benchmarks mentioned using @lmsysorg’s LLM Decontaminator.

The weights of our 70B model are available today on @huggingface here: https://huggingface.co/mattshumer/Reflection-70B

@hyperbolic_labs API available later today.

Next week, we will release the weights of Reflection-405B, along with a short report going into more detail on our process and findings.

Most importantly, a huge shoutout to @csahil28 and @GlaiveAI.

I’ve been noodling on this idea for months, and finally decided to pull the trigger a few weeks ago. I reached out to Sahil and the data was generated within hours.

If you’re training models, check Glaive out.

This model is quite fun to use and insanely powerful.

Please check it out — with the right prompting, it’s an absolute beast for many use-cases.

Demo here: https://reflection-playground-production.up.railway.app/

405B is coming next week, and we expect it to outperform Sonnet and GPT-4o by a wide margin.

But this is just the start. I have a few more tricks up my sleeve.

I’ll continue to work with @csahil28 to release even better LLMs that make this one look like a toy.

Stay tuned."

https://x.com/mattshumer_/status/1831767014341538166

https://archive.is/SXZMe

When training a transformer on positionally encoded embeddings, should the tgt output embeddings also be positionally encoded? If so, wouldn't the predicted/decoded embeddings also be positionally encoded?

Someone (Dreamertist on reddit) got tired of depending on Huggingface for downloading models and proposes a torrent tracker to share more efficiently these huge blobs.

It just started, only a few models uploaded yet, but I think it is worth that we all put our local stash online there. Making a new torrent is super easy (one missing step though: when "re-downloading" the model you need to save it in the directory where it already exists. This way it will "resume" at 100% completion and switch to seeding mode)

Imagine AI giving offsprings...

Hey guys,

I have been experimenting with self-supervised visual learning a bit. Until now I have only ever used U-Nets and related architectures.

No matter what specific task, images or other parameters I changed I always encountered these stains on my output-images (here marked with green), although sometimes more, sometimes less.

Now I wondered if anybody could tell me where they came from and how I could prevent them?

In the attached picture the input (left) and target (right) are the same, so that I can be sure these stains do not come from a badly designed learning task, yet they still appear (output is the middle image).

Thanks in advance and all the best :D

Edit: added line breaks