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Our infra lets us steer trillion-parameter frontier models in real time: - live, mid-CoT edits to internal activations - directly altering how the model reasons (not just outputs) - stackable edits - no added latency We can make models more Gen Z, more concise, etc.

29,647 次观看 • 9 个月前 •via X (Twitter)

7 条评论

Goodfire 的头像
Goodfire9 个月前

Thanks for having us @gopalkraman @spc!

Keate Pulse 的头像
Keate Pulse8 个月前

Exciting developments at @GoodfireAI! Real-time steering of AI models opens up new possibilities, especially in improving responsiveness and control. These innovations could redefine user interaction with AI systems! 🤖

Himanshu Kumar 的头像
Himanshu Kumar9 个月前

The system allows for live modification of internal activations with zero added latency.

Min Chon Chi 的头像
Min Chon Chi9 个月前

Directly altering how the model reasons seems very powerful.

justin curl 的头像
justin curl9 个月前

So cool!! how do these steering features interact with jailbreak resilience? I.e., if you steer it towards a feature that's inconsistent with safeguards, does it make it easier to jailbreak?

Veeraraju Elluru 的头像
Veeraraju Elluru9 个月前

This is so good!

BuBxPrivacy 🌊 RIVER|.edge🦭 的头像
BuBxPrivacy 🌊 RIVER|.edge🦭9 个月前

这是真的在改脑子啊

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learned a lot from this conversation with Simon Mo and Matt Bornstein. biggest takeaways for me: -there are a lot of reasons why we should like open-weight models. a lot of these arguments stop at handwavy things like "what if the labs stop releasing frontier models to the public" or "it's lower cost." but simon's position as lead maintainer of vLLM and CEO of Inferact give him authority to talk about some of the other, more interesting and concrete reasons to pay attention to open-weight models, namely that they allow end-users to calibrate latency / other performance metrics with way more customizability than what any of the frontier closed-source labs offer (and without the fear that your job might be met with a refusal at some random point where you're deep in a 2 hour job) -re: the above point...for this reason, a lot of US companies (inferact included!) choose to use open-weight models over their closed-source alternatives. this also isn't limited to internal workloads / research - on a recent a16z podcast the team at Decagon spoke about how something like 90% of their customer service ai agents run on open-weight models that they've fine-tuned. -we should really appreciate how many companies/teams came out researchers fascinated by the wave of very small open-weight models that were being distilled from e.g. gpt-3.5 and earlier models in 2022/2023 (prior to the release of chatGPT!). these small models motivated the development of pagedattention, which then led to vlmm/inferact (at other layers of the stack with similar origin stories, you can look at teams like openrouter or ollama). in other words, we have open-weight models to thank for a bunch of the orchestration infra we now rely on. i think yet another, indirect, way we can point to open-source/weight infra pushing the frontier forward. anyway, a lot more in this convo, it was a lot of fun!

Elena

12,922 次观看 • 1 个月前