Skip Navigation

InitialsDiceBearhttps://github.com/dicebear/dicebearhttps://creativecommons.org/publicdomain/zero/1.0/„Initials” (https://github.com/dicebear/dicebear) by „DiceBear”, licensed under „CC0 1.0” (https://creativecommons.org/publicdomain/zero/1.0/)H

hok

@ hok @lemmy.dbzer0.com

Posts
6
Comments
19
Joined
3 yr. ago

  • Which open models are actually good at agentic coding?

    Jump

  • Thank you for your opinion & recommendations. Something I saw today related to "sub-agents" is in Kimi 2.6's model card it says

    Elevated Agent Swarm: Scaling horizontally to 300 sub-agents executing 4,000 coordinated steps, K2.6 can dynamically decompose tasks into parallel, domain-specialized subtasks, delivering end-to-end outputs from documents to websites to spreadsheets in a single autonomous run.

    So maybe Kimi 2.6 is doing the "type of thing" I am looking for, but I don't have the means to run it practically. Maybe at 1 token per second which would be brutal.

    I tried out Qwen 3.6 27B but not yet in an agentic setting, so I can't really judge yet. Maybe it's just me but the small model size seems limiting. I thought gpt-oss-120b was good.

  • Which open models are actually good at agentic coding?

    Jump

  • What I have yet to learn is how much of the intelligence and accuracy comes from the model itself and how much comes from the agentic tool system. For example, my experience with ChatGPT probably would be much worse with the free version (no thinking or container).

  • crypt.fyi - Secure Secret Sharing with Zero-Knowledge End-to-End Encryption

    Jump

  • Also note that there's OnionShare, too. You don't need a TLS certificate or domain, don't need to port forward and can run it from home safely, routes over Tor so very hard to know you are even sharing something, well known and open source etc.

  • Why llms are suprisingly good at math, and what it means to process language.

    Jump

  • Thanks for the answer! I hadn't thought about asking for recipes based on the specific ingredients you have left.

  • Why llms are suprisingly good at math, and what it means to process language.

    Jump

  • More importantly, what was the recipe and was it any good?

  • Can you fine-tune on localized steering of an LLM?

    Jump

  • Thanks for your answer. I think to be clear, what I'm looking for is a kind of masked fine-tuning. You see, I want to "steer" a particular output instead of providing complete examples, which are costly to create.

    The steering would be something like this:

    1. I have an LLM generate a sequence.
    2. I find exactly where the LLM goes "off track" and correct it there (for only maybe 10-20 tokens instead of correcting the rest of the generation manually).
    3. The LLM continues "on track" until it goes off track again.

    What I would like to do is train the model based on these corrections I give it, where many corrections might be part of the same overall generation. Conceptually I think each correction must have some training value. I don't know much about masking, but what I mean here is that I don't want it to train on a few tens or hundreds of (incomplete) samples but rather thousands of (masked) "steers" that correct the course of the rest of the sample's generated text.

  • Can you fine-tune on localized steering of an LLM?

    Jump

  • Sorry, I really don't care to continue talking about the difference between supervised and unsupervised learning. It's a pattern used to describe how you are doing ML. It's not a property of a dataset (you wouldn't call Dataset A "unsupervised"). Read the Wikipedia articles for more details.

  • Can you fine-tune on localized steering of an LLM?

    Jump

  • No, in that case there's no labelling required. That would be unsupervised learning.

    https://en.wikipedia.org/wiki/Unsupervised_learning

    Conceptually, unsupervised learning divides into the aspects of data, training, algorithm, and downstream applications. Typically, the dataset is harvested cheaply "in the wild", such as massive text corpus obtained by web crawling, with only minor filtering (such as Common Crawl). This compares favorably to supervised learning, where the dataset (such as the ImageNet1000) is typically constructed manually, which is much more expensive.

  • Can you fine-tune on localized steering of an LLM?

    Jump

  • Ground truth labels are just prescriptive labels that we recognize as being true. The main thing that distinguishes unsupervised from supervised is that in unsupervised learning, what is "good" is learned from the unstructured data itself. In supervised learning, what is "good" is learned from some external input, like "good" human-provided examples.

  • Can you fine-tune on localized steering of an LLM?

    Jump

  • No, it's unsupervised. In pre-training, the text data isn't structured at all. It's books, documents, online sources, all put together.

    Supervised learning uses data with "ground truth" labels.

  • Can you fine-tune on localized steering of an LLM?

    Jump

  • This pre-training was done by Meta. It's what Llama-3.1-405B is (in contrast to Llama-3.1-405B-Instruct). https://huggingface.co/meta-llama/Llama-3.1-405B

    Training Data

    Overview: Llama 3.1 was pretrained on ~15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 25M synthetically generated examples.

  • Can you fine-tune on localized steering of an LLM?

    Jump

  • Unsupervised training happens during the pre-training phase when you dump all kinds of quality documents and it learns the relationship between tokens

  • Can you fine-tune on localized steering of an LLM?

    Jump

  • The article you linked to uses SFT (supervised fine tuning, a specific training technique) as its alignment strategy. There are other ways to fine-tune a model.

    I guess I'm wondering if you can train on these partial responses without needing the full rest of the output, without the stop token, or if you need full examples as the article hints to.

  • Can you fine-tune on localized steering of an LLM?

    Jump

  • Can SFT be used on partial generations? What I mean by a "steer" is a correction to only a portion, and not even the end, of model output.

    For example, a "bad" partial output might be:

     
        
<assistant> Here are four examples:
1. High-quality example 1
2. Low-quality example 2

    and the "steer" might be:

     
        
<assistant> Here are four examples:
1. High-quality example 1
2. High-quality example 2

    but the full response will eventually be:

     
        
<assistant> Here are four examples:
1. High-quality example 1
2. High-quality example 2
3. High-quality example 3
4. High-quality example 4

    The corrections don't include the full output.

  • LocalLLaMA @sh.itjust.works
    hok @lemmy.dbzer0.com

    Can you fine-tune on localized steering of an LLM?

  • Fish Speech 1.5, an open source voice cloning TTS that's actually good

    Jump

  • You are right. Their description of "SOTA Open Source TTS" caused me to assume it was open source, but it's clear that

    This codebase and all models are released under CC-BY-NC-SA-4.0 License.

    So, it's "source available" and not released under a permissive licence.

  • Llama 3.3 70b - End of open-weight pretrained models from Meta or just a better Llama 3.1 405b finetune?

    Jump

  • Thank you so much, that exactly answers my question with the official response (that guy works at Meta) that confirms it's the same base model!

    I was concerned primarily because in the release notes it strangely didn't mention it anywhere, and I thought it would have been important enough to mention.

  • LocalLLaMA @sh.itjust.works
    hok @lemmy.dbzer0.com

    Llama 3.3 70b - End of open-weight pretrained models from Meta or just a better Llama 3.1 405b finetune?

  • Fish Speech 1.5, an open source voice cloning TTS that's actually good

    Jump

  • I followed their instructions here: https://speech.fish.audio/

    I am using the locally-run API server to do inference: https://speech.fish.audio/inference/#http-api-inference

    I don't know about other ways. To be clear, this is not (necessarily) an LLM, it's just for speech synthesis, so you don't run it on ollama. That said I think it does technically use Llama under the hood since there are two models, one for encoding text and the other for decoding to audio. Honestly the paper is terrible but it explains the architecture somewhat: https://arxiv.org/pdf/2411.01156

  • Free Open-Source Artificial Intelligence @lemmy.world
    hok @lemmy.dbzer0.com

    Fish Speech 1.5, an open source voice cloning TTS that's actually good

    github.com /fishaudio/fish-speech

  • Discussing jury nullification is against the ToS of lemmy.world and will get you banned, according to worldnews mod

    Jump

  • On Lemmy, everything is a bit leftist at the moment.

  • Stable Diffusion @lemmy.dbzer0.com
    hok @lemmy.dbzer0.com

    What models can we use for img2img today?