Seems Meta have been doing some research lately, to replace the current tokenizers with new/different representations:

• Large Concept Models: Language Modeling in a Sentence Representation Space [Github] (December 11, 2024)
• Byte Latent Transformer: Patches Scale Better Than Tokens [Github] (December 12, 2024)

Absolutely humongous model. Mixture of 256 experts with 8 activated each time.

Aider leaderboard: The only model above 🐋 v3 here is ~~Open~~AI o1. DeepSeek is known to make amazing models and Aider rotates their benchmark over time, so it is unlikely that this is a train-on-benchmark situation.

Some more benchmarks: on Reddit.

I want to fine tune an LLM to "steer" it in the right direction. I have plenty of training examples in which I stop the generation early and correct the output to go in the right direction, and then resume generation.

Basically, for my dataset doing 100 "steers" on a single task is much cheaper than having to correct 100 full generations completely, and I think each of these "steer" operations has value and could be used for training.

So maybe I'm looking for some kind of localized DPO. Does anyone know if something like this exists?

Howdy!

(moved this comment from the noob question thread because no replies)

I'm not a total noob when it comes to general compute and AI. I've been using online models for some time, but I've never tried to run one locally.

I'm thinking about buying a new computer for gaming and for running/testing/developing LLMs (not training, only inference and in context learning) . My understanding is that ROCm is becoming decent (and I also hate Nvidia) , so I'm thinking that a Radeon Rx 7900 XTX might be a good start. If I buy the right motherboard I should be able to put another XTX in there as well, later. If I use watercooling.

So first, what do you think about this? Are the 24 gigs of VRAM worth the extra bucks? Or should I just go for a mid-range GPU like the Arc B580?

I'm also curious experimenting with a no-GPU setup. I.e. CPU + lots of RAM. What kind of models do you think I'll be able to run, with decent performance, if I have something like a Ryzen 7 9800X3D and 128/256 GB of DDR5? How does it compare to the Radeon RX 7900 XTX? Is it possible to utilize both CPU and GPU when running inference with a single model, or is it either or?

Also.. Is it not better if noobs post questions in the main thread? Then questions will probably reach more people. It's not like there is super much activity..

Seriously though, does anyone know how to use openwebui with the new version?

Edit: if you go into the ollama container using sudo docker exec -it bash, then you can pull models with ollama pull llama3.1:8b for example and have it.

People are talking about the new Llama 3.3 70b release, which has generally better performance than Llama 3.1 (approaching 3.1's 405b performance): https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_3

However, something to note:

Llama 3.3 70B is provided only as an instruction-tuned model; a pretrained version is not available.

Is this the end of open-weight pretrained models from Meta, or is Llama 3.3 70b instruct just a better-instruction-tuned version of a 3.1 pretrained model?

Comparing the model cards: 3.1: https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md 3.3: https://github.com/meta-llama/llama-models/blob/main/models/llama3_3/MODEL_CARD.md

The same knowledge cutoff, same amount of training data, and same training time give me hope that it's just a better finetune of maybe Llama 3.1 405b.

I've been working on keeping the OSM tool up to date for OpenWebUI's rapid development pace. And now I've added better-looking citations, with fancy styling. Just a small announcement post!

Update: when this was originally posted, the tool was on 1.3. Now it's updated to 2.1.0, with a navigation feature (beta) and more fixes for robustness.

I've been using Qwen 2.5 Coder (bartowski/Qwen2.5.1-Coder-7B-Instruct-GGUF) for some time now, and it has shown significant improvements compared to previous open weights models.

Notably, this is the first model that can be used with Aider. Moreover, Qwen 2.5 Coder has made notable strides in editing files without requiring frequent retries to generate in the proper format.

One area where most models struggle, including this one, is when the prompt exceeds a certain length. In this case, it appears that the model becomes unable to remember the system prompt when the prompt length is above ~2000 tokens.

I'm just a hobbyist in this topic, but I would like to share my experience with using local LLMs for very specific generative tasks.

Predefined formats

With prefixes, we can essentially add the start of the LLMs response, without it actually generating it. Like, when We want it to Respond with bullet points, we can set the prefix to be - (A dash and space). If we want JSON, we can use ` ` `json\n{ as a prefix, to make it think that it started a JSON markdown code block.

If you want a specific order in which the JSON is written, you can set the prefix to something like this:

` ` `json
{
    "first_key":

Translation

Let's say you want to translate a given text. Normally you would prompt a model like this

Translate this text into German:
` ` `plaintext
[The text here]
` ` `
Respond with only the translation!

Or maybe you would instruct it to respond using JSON, which may work a bit better. But what if it gets the JSON key wrong? What if it adds a little ramble infront or after the translation? That's where prefixes come in!

You can leave the promopt exactly as is, maybe instructing it to respond in JSON

Respond in this JSON format:
{"translation":"Your translation here"}

Now, you can pretend that the LLM already responded with part of the message, which I will call a prefix. The prefix for this specific usecase could be this:

{
    "translation":"

Now the model thinks that it already wrote these tokens, and it will continue the message from right where it thinks it left off. The LLM might generate something like this:

Es ist ein wunderbarer Tag!"
}

To get the complete message, simply combine the prefix and the generated text to result in this:

{
    "translation":"Es ist ein wunderschöner Tag!"
}

To minimize inference costs, you can add "} and "\n} as stop tokens, to stop the generation right after it finished the json entrie.

Code completion and generation

What if you have an LLM which didn't train on code completion tokens? We can get a similar effect to the trained tokens using an instruction and a prefix!

The prompt might be something like this

` ` `python
[the code here]
` ` `
Look at the given code and continue it in a sensible and reasonable way.
For example, if I started writing an if statement,
determine if an else statement makes sense, and add that.

And the prefix would then be the start of a code block and the given code like this

` ` `python
[the code here]

This way, the LLM thinks it already rewrote everything you did, but it will now try to complete what it has written. We can then add \n` ` ` as a stop token to make it only generate code and nothing else.

This approach for code generation may be more desireable, as we can tune its completion using the prompt, like telling it to use certain code conventions.

Simply giving the model a prefix of ` ` `python\n Makes it start generating code immediately, without any preamble. Again, adding the stop keyword \n` ` ` makes sure that no postamble is generated.

Using this in ollama

Using this "technique" in ollama is very simple, but you must use the /api/chat endpoint and cannot use /api/generate. Simply append the start of a message to the conversation passed to the model like this:

"conversation":[
    {"role":"user", "content":"Why is the sky blue?"},
    {"role":"assistant", "content":"The sky is blue because of"}
]

It's that simple! Now the model will complete the message with the prefix you gave it as "content".

Be aware!

There is one pitfall I have noticed with this. You have to be aware of what the prefix gets tokenized to. Because we are manually setting the start of the message ourselves, it might not be optimally tokenized. That means, that this might confuse the LLM and generate one too many or few spaces. This is mostly not an issue though, as

What do you think? Have you used prefixes in your generations before?

Looks interesting. Love seeing more coming out from this space

https://huggingface.co/collections/Qwen/qwen25-66e81a666513e518adb90d9e

Qwen 2.5 0.5B, 1.5B, 3B, 7B, 14B, 32B, and 72B just came out, with some variants in some sizes just for math or coding, and base models too.

All Apache licensed, all 128K context, and the 128K seems legit (unlike Mistral).

And it's pretty sick, with a tokenizer that's more efficient than Mistral's or Cohere's and benchmark scores even better than llama 3.1 or mistral in similar sizes, especially with newer metrics like MMLU-Pro and GPQA.

I am running 34B locally, and it seems super smart!

As long as the benchmarks aren't straight up lies/trained, this is massive, and just made a whole bunch of models obsolete.

Get usable quants here:

GGUF: https://huggingface.co/bartowski?search_models=qwen2.5

EXL2: https://huggingface.co/models?sort=modified&search=exl2+qwen2.5

Mistral Small 22B just dropped today and I am blown away by how good it is. I was already impressed with Mistral NeMo 12B's abilities, so I didn't know how much better a 22B could be. It passes really tough obscure trivia that NeMo couldn't, and its reasoning abilities are even more refined.

With Mistral Small I have finally reached the plateu of what my hardware can handle for my personal usecase. I need my AI to be able to at least generate around my base reading speed. The lowest I can tolerate is 1.5~T/s lower than that is unacceptable. I really doubted that a 22B could even run on my measly Nvidia GTX 1070 8G VRRAM card and 16GB DDR4 RAM. Nemo ran at about 5.5t/s on this system, so how would Small do?

Mistral Small Q4_KM runs at 2.5T/s with 28 layers offloaded onto VRAM. As context increases that number goes to 1.7T/s. It is absolutely usable for real time conversation needs. I would like the token speed to be faster sure, and have considered going with the lowest Q4 recommended to help balance the speed a little. However, I am very happy just to have it running and actually usable in real time. Its crazy to me that such a seemingly advanced model fits on my modest hardware.

Im a little sad now though, since this is as far as I think I can go in the AI self hosting frontier without investing in a beefier card. Do I need a bigger smarter model than Mistral Small 22B? No. Hell, NeMo was serving me just fine. But now I want to know just how smart the biggest models get. I caught the AI Acquisition Syndrome!

I just found https://www.arliai.com/ who offer LLM inference for quite cheap. Without rate-limits and unlimited token generation. No-logging policy and they have an OpenAI compatible API.

I've been using runpod.io previously but that's a whole different service as they sell compute and the customers have to build their own Docker images and run them in their cloud, by the hour/second.

Should I switch to ArliAI? Does anyone have some experience with them? Or can recommend another nice inference service? I still refuse to pay $1.000 for a GPU and then also pay for electricity when I can use some $5/month cloud service and it'd last me 16 years before I reach the price of buying a decent GPU...

Edit: Saw their $5 tier only includes models up to 12B parameters, so I'm not sure anymore. For larger models I'd need to pay close to what other inference services cost.

Edit2: I discarded the idea. 7B parameter models and one 12B one is a bit small to pay for. I can do that at home thanks to llama.cpp

I'm currently using SuperNormal to taking meeting minutes for all of my Teams, Google Meet, and Zoom conference calls. Is there a workflow for doing this locally with Whisper and some other tools? I haven't found one yet.

Only recently did I discover the text-to-music AI companies (udio.com, suno.com) and I was surprised about how good the results are. Both are under lawsuit from RIAA.

I am curious if there are any local ones I can experiment with or train myself. I know there is facebook/musicgen-large on HuggingFace. That model is over 1 year old and there might be others by now. Also, based on the card I get the feeling that model is not going to be good at doing specific song lyrics (maybe the lyrics just were absent from the training data?). I am most interested in trying my hand at writing songs and fine-tuning a model on specific types of music to get the sounds I am looking for.

Another day, another model.

Just one day after Meta released their new frontier models, Mistral AI surprised us with a new model, Mistral Large 2.

It's quite a big one with 123B parameters, so I'm not sure if I would be able to run it at all. However, based on their numbers, it seems to come close to GPT-4o. They claim to be on par with GPT-4o, Claude 3 Opus, and the fresh Llama 3 405B regarding coding related tasks.

It's multilingual, and from what they said in their blog post, it was trained on a large coding data set as well covering 80+ programming languages. They also claim that it is "trained to acknowledge when it cannot find solutions or does not have sufficient information to provide a confident answer"

On the licensing side, it's free for research and non-commercial applications, but you have to pay them for commercial use.

Meta has released llama 3.1. It seems to be a significant improvement to an already quite good model. It is now multilingual, has a 128k context window, has some sort of tool chaining support and, overall, performs better on benchmarks than its predecessor.

With this new version, they also released their 405B parameter version, along with the updated 70B and 8B versions.

I've been using the 3.0 version and was already satisfied, so I'm excited to try this.

Hello y'all, i was using this guide to try and set up llama again on my machine, i was sure that i was following the instructions to the letter but when i get to the part where i need to run setup_cuda.py install i get this error

File "C:\Users\Mike\miniconda3\Lib\site-packages\torch\utils\cpp_extension.py", line 2419, in _join_cuda_home raise OSError('CUDA_HOME environment variable is not set. ' OSError: CUDA_HOME environment variable is not set. Please set it to your CUDA install root. (base) PS C:\Users\Mike\text-generation-webui\repositories\GPTQ-for-LLaMa>

i'm not a huge coder yet so i tried to use setx to set CUDA_HOME to a few different places but each time doing echo %CUDA_HOME doesn't come up with the address so i assume it failed, and i still can't run setup_cuda.py

Anyone have any idea what i'm doing wrong?

You type "Once upon a time!!!!!!!!!!" and those exclamation marks are rendered to show the LLM generated text, using a tiny 30MB model

via https://simonwillison.net/2024/Jun/23/llama-ttf/

Hello! I am looking for some expertise from you. I have a hobby project where Phi-3-vision fits perfectly. However, the PyTorch version is a little too big for my 8GB video card. I tried looking for a quantized model, but all I found is 4-bit. Unfortunately, this model works too poorly for me. So, for the first time, I came across the task of quantizing a model myself. I found some guides for Phi-3V quantization for ONNX. However, the only options are fp32(?), fp16, int4. Then, I found a nice tool for AutoGPTQ but couldn't make it work for the job yet. Does anybody know why there is no int8/int6 quantization for Phi-3-vision? Also, has anybody used AutoGPTQ for quantization of vision models?

"Alice has N brothers and she also has M sisters. How many sisters does Alice’s brother have?"

The problem has a light quiz style and is arguably no challenge for most adult humans and probably to some children.

The scientists posed varying versions of this simple problem to various State-Of-the-Art LLMs that claim strong reasoning capabilities. (GPT-3.5/4/4o , Claude 3 Opus, Gemini, Llama 2/3, Mistral and Mixtral, including very recent Dbrx and Command R+)

They observed a strong collapse of reasoning and inability to answer the simple question as formulated above across most of the tested models, despite claimed strong reasoning capabilities. Notable exceptions are Claude 3 Opus and GPT-4 that occasionally manage to provide correct responses.

This breakdown can be considered to be dramatic not only because it happens on such a seemingly simple problem, but also because models tend to express strong overconfidence in reporting their wrong solutions as correct, while often providing confabulations to additionally explain the provided final answer, mimicking reasoning-like tone but containing nonsensical arguments as backup for the equally nonsensical, wrong final answers.

• Arxiv, paper submitted on 4 Jun 2024
• PDF
• heise online article (German)

Remember 2-3 years ago when OpenAI had a website called transformer that would complete a sentence to write a bunch of text. Most of it was incoherent but I think it is important for historic and humor purposes.