Earlier quoted context omitted.
not sure why youre fixed on censoring. if we invert your POV censoring includes not reporting falsehoods "vaccines are harmful". Science and logic often tackle these subject via censoring, but a model given a equal sampling of Internet, would think vacinnes are harmful. a less naive correction would censor this problematic context. so im cofised as to why you think unmasking whatever bias you think is censored will r…
This is something difficult to handle properly. I think it is useful to turn off censoring if you need. When I am researching something, I likely want proper information. If I am looking up information on vaccines, I don't want information that crackpots spread online on chips on vaccines and how 5g will kill the vaccinated, or how it is somehow connected with Bill Gates spreading meat allergies through drones rainin…
DeepSeek-V4-Flash means LLM steering is interesting again
31–40 of 84 posts
Re: DeepSeek-V4-Flash means LLM steering is interesting again
#32I'm surprised the article doesn't mention the biggest use of steering vectors, which is the potential to remove refusals from models (a.k.a. abliteration or uncensoring). There was an earlier paper that found that "most refusals are on a single vector", and you can identify and "nerf" that vector so the model will skip refusals and answer "any" request normally. This was very doable for earlier models trained with SF…
not sure why youre fixed on censoring. if we invert your POV censoring includes not reporting falsehoods "vaccines are harmful". Science and logic often tackle these subject via censoring, but a model given a equal sampling of Internet, would think vacinnes are harmful. a less naive correction would censor this problematic context. so im cofised as to why you think unmasking whatever bias you think is censored will r…
Re: DeepSeek-V4-Flash means LLM steering is interesting again
#33Re: DeepSeek-V4-Flash means LLM steering is interesting again
#34Re: DeepSeek-V4-Flash means LLM steering is interesting again
#35Earlier quoted context omitted.
That's not what people mean when they talk about censoring. They mean that models are trained to not touch some subjects, and that can spill over in legit tasks, often with humorous results (early on, there were many instances of models refusing to answer "how do you kill a process", because of overbearing refusal training). Uncensoring a model also doesn't necessarily improve generic use cases. In fact it can lead t…
> Uncensoring a model also doesn't necessarily improve generic use cases. While the following is not a generic use case, I have a funny anecdote about how censorship is holding back flagship models. I was asking an uncensored version of Qwen3.6 how a CLI option of llama.cpp worked, and to my horror and amazement, it rudely went and decompiled the binary to figure it out. It felt like the computer-equivalent of asking…
Just a data point, but I’ve been having Claude do this regularly
Re: DeepSeek-V4-Flash means LLM steering is interesting again
#36Earlier quoted context omitted.
> Uncensoring a model also doesn't necessarily improve generic use cases. While the following is not a generic use case, I have a funny anecdote about how censorship is holding back flagship models. I was asking an uncensored version of Qwen3.6 how a CLI option of llama.cpp worked, and to my horror and amazement, it rudely went and decompiled the binary to figure it out. It felt like the computer-equivalent of asking…
> Flagship models usually do not do that without some convincing Just a data point, but I’ve been having Claude do this regularly
Re: DeepSeek-V4-Flash means LLM steering is interesting again
#37I know it's only tangentially relevant, but I've been baffled by the interest in DeepSeek V4 Flash. It's larger, less efficient, and in many cases, performs worse on both objective benchmarks and real world sniff test (admittedly, n=1) than Minimax M2.7. DS4F hallucinates at extraordinary rates while M2.7 does not. The 196k context length that M2.7 was natively trained up represents neither a hard technical ceiling (…
DS4 also has some neat new arch improvements, giving it a lot of context at lower VRAM usage. So it will be cheaper to serve, B for B than previous models.
Re: DeepSeek-V4-Flash means LLM steering is interesting again
#38Earlier quoted context omitted.
> Uncensoring a model also doesn't necessarily improve generic use cases. While the following is not a generic use case, I have a funny anecdote about how censorship is holding back flagship models. I was asking an uncensored version of Qwen3.6 how a CLI option of llama.cpp worked, and to my horror and amazement, it rudely went and decompiled the binary to figure it out. It felt like the computer-equivalent of asking…
> Flagship models usually do not do that without some convincing Just a data point, but I’ve been having Claude do this regularly
Re: DeepSeek-V4-Flash means LLM steering is interesting again
#39Write up: https://www.outcryai.com/research/shift-a-models-political-i...
App: https://apps.apple.com/us/app/outcry-activist-ai/id676208676...
This technique has a lot of potential.
Re: DeepSeek-V4-Flash means LLM steering is interesting again
#40I know it's only tangentially relevant, but I've been baffled by the interest in DeepSeek V4 Flash. It's larger, less efficient, and in many cases, performs worse on both objective benchmarks and real world sniff test (admittedly, n=1) than Minimax M2.7. DS4F hallucinates at extraordinary rates while M2.7 does not. The 196k context length that M2.7 was natively trained up represents neither a hard technical ceiling (…
M2.7 is no longer open source, it's been changed to a NC license. It's an OK model, but IME out of the big 5 chinese models (ds, glm, kimi, minimax and qwen), DS models have generally shown better generalisation and real-world usage than all the others, even if the benchmark scores were lower. Less benchmaxxxing, basically. DS4 also has some neat new arch improvements, giving it a lot of context at lower VRAM usage.…
The weights may nominally be legally copyrighted, but the rightsholder certainly doesn't seem to be making anything resembling a serious effort to actually assert or defend those rights; on the contrary, they are doing the exact opposite by maximizing the gratis distribution, including knowingly and willingly via third parties, with no copy protection whatsoever, and no reasonable expectation of non-distribution.
They are not behaving like an entity trying to protect valuable intellectual property, they are behaving like an entity trying to reap the reputational and network effect benefits of maximizing the free distribution of a public good.
Less memory usage by the KV cache doesn't mean cheaper to serve overall. Once you've acquired hardware (for which you need more to serve DS4L than Minimax M2.7, the former being ~54B total params larger model to begin with, and which KV cache memory efficiency does nothing to address), the capex cost is basically fixed and opex just comes down to power draw, which will be marginally higher per token with DS4L than with M2.7 owed to the slower speeds that result from 13B active params vs 10B active params on forward passes during TG.