Many new projects still use https://github.com/TransformerLensOrg/TransformerLens as a core dependency. Over 500 public code repositories on Github rely upon the transformer-lens package, including ones created by leading organizations such as Meta Research, Redwood Research, Model Evaluation & Threat Research, Apollo Research, and Decode Research.

Bryce has a proven track record of consistent contributions to the library and is the best possible owner for ensuring the stability and growth of TransformerLens moving forward (as well as compatibility across other packages for doing mechinterp). He brings a deep expert understanding of the existing feature set and has demonstrated rapid iteration speed and skill to adapt the framework for recently launched models.

He also actively volunteers to answer community questions on Slack and integrates user feedback into the development roadmap with detailed progress updates to relevant stakeholders. I would also be excited to see Fabian's contributions going into TransformerLens 3.0 and beyond. I'd endorse and highly recommend donating to this manifund project to help it reach its full funding goal through the rest of 2025.

I would suggest reaching out to Andrej Karpathy: https://karpathy.bearblog.dev/finding-the-best-sleep-tracker/

typo:

payments go directly directly -> payments go directly

I am glad that I found this before the deadline to contribute.

That's fine.

@ScottAlexander Is it possible to hold on to my current shares? Not interested in selling at the moment.

How much money have you spent so far?

• It’s hard to calculate this but I’d claim it’s about USD 10k. More if you include opportunity costs. I can provide a breakdown of this budget upon request.

Have you gotten more funding from other sources?

• Yes. Janus has provided OpenAI API credits and has reimbursed some of my other expenses. Nuño has been consulting. For the rest, I’ve drawn from savings by selling RSUs. 

How is the project going?

• Got accepted to SPAR under Rubi Hudson, so this project is merging with Avoiding Incentives for Performative Prediction in AI | Manifund

• Plan to continue working on this agenda from Jan to Apr 2024, sent an application to AI Safety Camp

• Ran some basic experiments but bottlenecked on conceptual progress. Some false starts, no publishable artifacts so far, but working on it. Please get in touch directly if you'd like to hear more.

How well has your project gone compared to where you expected it to be? (Score from 1-10, 10 = Better than expected)

• 3.3

Are there any remaining ways you need help, besides more funding?

• A magic wand that reduces bureaucratic inefficiency.

Any other thoughts or feedback?

•  Not for now!

I believe this project is so promising that I applied to SPAR to volunteer to help directly.

Briefly: Got access to the base model of GPT-4, trying to explore why it’s better calibrated than the instruction fine-tuned RLHF version. Also in DMs with the CEO of Lambda Labs to discuss renting H100s. I’ll fly out to Berkeley from July 10th to Sep 7 if I get a U.S visa. Collaborating with the Cyborgism stream. I’m also transferring teams to work on Bing Chat and am trying to get researcher access to GPT-4’s vision module.

Primary expense at this stage is the cost of our time. More investment would be a signal that this work is valuable, which would make it easier to prioritize over alternative projects.

Further progress is not blocked on funding, but would accelerate it, although I can’t claim to know what the precise relationship is there.

We would likely spend the money to free up more focus time.

Updates:

• The Autocast Competition (mlsafety.org) was closed due to the FTX collapse, so we decided to scrap the paper and reorient towards eventually selling the project to Anthropic instead.

• No outputs on the development side in the last two weeks because I needed a break after pushing to wrap up work prior to my vacation and continuous exhaustion isn't sustainable.

• Applied to SERI MATS to get more time to work on this, got an informal accept from the mentor we targeted, but waiting for official decisions to be out.

@Austin thanks! Quick answers:

Deliverables: We'll open source our methods, code, models, data, animations, and any additional information needed to reproduce the experimental results. We aim to submit a paper to NeurIPS 2023 within the next 8-9 weeks. Public release date is currently 14 weeks from now.

Commitment: I am taking 4 weeks off (starting late April) to focus primarily on this project. As far as when to scale: it's hard to give a firm date since the field moves so fast, but this is really a function of how much we raise. Some parts of our architecture are scale invariant, others plug into publicly available LLMs, and some components of the system are traditional software. On the margin, dollars spent on inference and evaluation (for e.g ablation studies/prompt testing) are more useful than dollars spent on training, at least until you get pretty far down the list of ideas. We'll make the decision to scale when we think it's a good idea, and we don't yet know precisely when that will be.

@aaronl yep! Mostly along the lines of [2206.15474] Forecasting Future World Events with Neural Networks (arxiv.org)