Good to know, thanks!

Ah that must be it sorry. I thought they had decorelated phone numbers and IDs

Groups have an encryption key that I guess you receive from other members upon joining.

Spaces is an underused feature that I hope see gain more traction! It makes Matrix a credible competitor to Slack and Discord

Not really, have used it for years like that. But you need to set it up initially on your phone. The newish feature (less than a year) is that I think they do not require a phone number to set up a new account.

I saw a year ago a comment I often think about, which is that India’s economy, where a lot of call center and remote workers are, is “token-based”. LLMs are going to hurt their labor but they are also the best placed to profit off LLMs, having already many established consumers.

Install text-generation-webui, check their “whisper stt” option, and you can talk with a computer. As a non native I prefer to read the english output than listen to it but they do provide TTS as well.

It is called finetuning. I haven’t tried it but oobagooba’s text-generation-webui has a tab to do it and I believe it is pretty straightforward.

Fine tune a base model on your dataset and then tou will then need to format your prompt in the way your AIM logs are organized. e.g. you will need to add “<ch00f>” add the end of your text completion task. It will complete it in the way it learnt it.

If you don’t have a the GPU for it, many companies offer fine-tuning as a service like Mistral

It is llama3-8B so it is not out of question but I am not sure how much memory you would need to really go to 1M context window. They use ring attention to achieve high context window, which I am unfamiliar with but that seems to lower greatly the memory requirements.

To actually read how they did it, here is there model page: https://huggingface.co/gradientai/Llama-3-8B-Instruct-Gradient-1048k

Approach:

• meta-llama/Meta-Llama-3-8B-Instruct as the base
• NTK-aware interpolation [1] to initialize an optimal schedule for RoPE theta, followed by empirical RoPE theta optimization
• Progressive training on increasing context lengths, similar to Large World Model [2] (See details below)

Infra

We build on top of the EasyContext Blockwise RingAttention library [3] to scalably and efficiently train on contexts up to 1048k tokens on Crusoe Energy high performance L40S cluster.

Notably, we layered parallelism on top of Ring Attention with a custom network topology to better leverage large GPU clusters in the face of network bottlenecks from passing many KV blocks between devices. This gave us a 33x speedup in model training (compare 524k and 1048k to 65k and 262k in the table below).

Data

For training data, we generate long contexts by augmenting SlimPajama. We also fine-tune on a chat dataset based on UltraChat [4], following a similar recipe for data augmentation to [2].

“Theft” is actually legal. Sharing (what they call “piracy”) is not. How about getting the fucking copyright reform that we should have done two decades ago?

OpenAI should be fine. They are leaders but there are plenty of competitors.

Microsoft is in a much more dominant situation and will have to argue that Google competes with them, which is true but may be hard to sell given the fact that I dont think Google offers its TPU services to any other company.

NVidia is in a situation of monopoly. For them it will be hard to argue otherwise. AMD is simply not there, no one using it.

And this is why research is going in another direction: smaller models which allow easier experiments.

There is a company-wide demotivation plague at Google. Don’t blame middle manager, it extends to the top.

I use it almost daily.

It does produce good code. It does not reliably produce good code. I am a programmer, it makes my job 10x faster and I just have to fix a few bugs in the code it usually generates. Over time, I learned what it is good at (UI code, converting things, boilerplate) and what it struggles with (anything involving newer tech, algorithmic understanding, etc.)

I often refer to it as my intern: It acts like an academically trained, not particularly competent, but very motivated, fast typing intern.

But then I am also working on the field. Prompting it correctly is too often dismissed as a skill (I used to dismiss it too). It needs more understanding than people give it credit for.

I think that like many IT tech it will go from being a dev tool to everyday tool gradually.

All the pieces of the puzzle to be able to control a computer by voice using only natural language are there. You don’t realize how big it is. Companies haven’t assembled it yet because it is actually harder to monetize on it than code it. I think probably Apple is in the best position for it. Microsoft is going to attempt and will fail like usual and Google will probably put a half-assed attempt at it. I’ll personally go for the open source version of it.

Over my dead body.

That’s actually super interesting and potentially game changing! thanks!

Can’t wait! But really, this type of things is what makes it hard for me to cheer at AMD:

For reasons unknown to me, AMD decided this year to discontinue funding the effort and not release it as any software product. But the good news was that there was a clause in case of this eventuality: Janik could open-source the work if/when the contract ended.

I wish we had a champion of openness but in that respect AMD just looks like a worse version of NVidia. Hell, even Intel has been a better player!

That’s the opposite of the feedback I got. AMD claims to support all of the transformers library but many people report this to be a lie.

I am in no love of companies that establish de-facto monopolices, but that is indeed what NVidia has right now. Everything is built over CUDA, AMD has a lot of catch-up to do.

I have the impression that Apple chips support more things than AMD does.

There are some people making things work on AMD, and I cheer to them, but let’s not pretend it is as easy as with Nvidia. Most packages depend on cuda for gpu acceleration.

I would suggest to maybe use it more on imaginary rendition, of fiction or literature. Or on colorizing some specific styles like engravings.