Llama Alternatives

llama reviews and mentions

• You Wouldn't Download an AI
  2 projects | news.ycombinator.com | 5 Jan 2025

  IANAL But, this is not true it would be a piece of the software. If there is a copyright on the app itself it would extend to the model. Even models have licenses for example LLAMA is release under this license [1]

  [1] https://github.com/meta-llama/llama/blob/main/LICENSE

• LM Studio 0.3.0
  6 projects | news.ycombinator.com | 24 Aug 2024

  Hello Hacker News, Yagil here- founder and original creator of LM Studio (now built by a team of 6!). I had the initial idea to build LM Studio after seeing the OG LLaMa weights ‘leak’ (https://github.com/meta-llama/llama/pull/73/files) and then later trying to run some TheBloke quants during the heady early days of ggerganov/llama.cpp. In my notes LM Studio was first “Napster for LLMs” which evolved later to “GarageBand for LLMs”.

  What LM Studio is today is a an IDE / explorer for local LLMs, with a focus on format universality (e.g. GGUF) and data portability (you can go to file explorer and edit everything). The main aim is to give you an accessible way to work with LLMs and make them useful for your purposes.

  Folks point out that the product is not open source. However I think we facilitate distribution and usage of openly available AI and empower many people to partake in it, while protecting (in my mind) the business viability of the company. LM Studio is free for personal experimentation and we ask businesses to get in touch to buy a business license.

  At the end of the day LM Studio is intended to be an easy yet powerful tool for doing things with AI without giving up personal sovereignty over your data. Our computers are super capable machines, and everything that can happen locally w/o the internet, should. The app has no telemetry whatsoever (you’re welcome to monitor network connections yourself) and it can operate offline after you download or sideload some models.

  0.3.0 is a huge release for us. We added (naïve) RAG, internationalization, UI themes, and set up foundations for major releases to come.

• Open Source AI Is the Path Forward
  7 projects | news.ycombinator.com | 23 Jul 2024
• Mark Zuckerberg: Llama 3, $10B Models, Caesar Augustus, Bioweapons [video]
  3 projects | news.ycombinator.com | 18 Apr 2024

  derivative works thereof).”

  https://github.com/meta-llama/llama/blob/b8348da38fde8644ef0...

  Also even if you did use Llama for something, they could unilaterally pull the rug on you when you got 700 million years, AND anyone who thinks Meta broke their copyright loses their license. (Checking if you are still getting screwed is against the rules)

  Therefore, Zuckerberg is accountable for explicitly anticompetitive conduct, I assumed an MMA fighter would appreciate the value of competition, go figure.

• Hello OLMo: A Open LLM
  3 projects | news.ycombinator.com | 8 Apr 2024

  One thing I wanted to add and call attention to is the importance of licensing in open models. This is often overlooked when we blindly accept the vague branding of models as “open”, but I am noticing that many open weight models are actually using encumbered proprietary licenses rather than standard open source licenses that are OSI approved (https://opensource.org/licenses). As an example, Databricks’s DBRX model has a proprietary license that forces adherence to their highly restrictive Acceptable Use Policy by referencing a live website hosting their AUP (https://github.com/databricks/dbrx/blob/main/LICENSE), which means as they change their AUP, you may be further restricted in the future. Meta’s Llama is similar (https://github.com/meta-llama/llama/blob/main/LICENSE ). I’m not sure who can depend on these models given this flaw.

• Reaching LLaMA2 Performance with 0.1M Dollars
  2 projects | news.ycombinator.com | 4 Apr 2024

  It looks like Llama 2 7B took 184,320 A100-80GB GPU-hours to train[1]. This one says it used a 96×H100 GPU cluster for 2 weeks, for 32,256 hours. That's 17.5% of the number of hours, but H100s are faster than A100s [2] and FP16/bfloat16 performance is ~3x better.

  If they had tried to replicate Llama 2 identically with their hardware setup, it'd cost a little bit less than twice their MoE model.

  [1] https://github.com/meta-llama/llama/blob/main/MODEL_CARD.md#...

• DBRX: A New Open LLM
  6 projects | news.ycombinator.com | 27 Mar 2024

  Ironically, the LLaMA license text [1] this is lifted verbatim from is itself copyrighted [2] and doesn't grant you the permission to copy it or make changes like s/meta/dbrx/g lol.

  [1] https://github.com/meta-llama/llama/blob/main/LICENSE#L65

• How Chain-of-Thought Reasoning Helps Neural Networks Compute
  1 project | news.ycombinator.com | 22 Mar 2024

  This is kind of an epistemological debate at this level, and I make an effort to link to some source code [1] any time it seems contentious.

  LLMs (of the decoder-only, generative-pretrained family everyone means) are next token predictors in a literal implementation sense (there are some caveats around batching and what not, but none that really matter to the philosophy of the thing).

  But, they have some emergent behaviors that are a trickier beast. Probably the best way to think about a typical Instruct-inspired “chat bot” session is of them sampling from a distribution with a KL-style adjacency to the training corpus (sidebar: this is why shops that do and don’t train/tune on MMLU get ranked so differently than e.g. the arena rankings) at a response granularity, the same way a diffuser/U-net/de-noising model samples at the image batch (NCHW/NHWC) level.

  The corpus is stocked with everything from sci-fi novels with computers arguing their own sentience to tutorials on how to do a tricky anti-derivative step-by-step.

  This mental model has adequate explanatory power for anything a public LLM has ever been shown to do, but that only heavily implies it’s what they’re doing.

  There is active research into whether there is more going on that is thus far not conclusive to the satisfaction of an unbiased consensus. I personally think that research will eventually show it’s just sampling, but that’s a prediction not consensus science.

  They might be doing more, there is some research that represents circumstantial evidence they are doing more.

  [1] https://github.com/meta-llama/llama/blob/54c22c0d63a3f3c9e77...

• Asking Meta to stop using the term "open source" for Llama
  1 project | news.ycombinator.com | 28 Feb 2024
• Markov Chains Are the Original Language Models
  2 projects | news.ycombinator.com | 1 Feb 2024

  Predicting subsequent text is pretty much exactly what they do. Lots of very cool engineering that’s a real feat, but at its core it’s argmax(P(token|token,corpus)):

  https://github.com/facebookresearch/llama/blob/main/llama/ge...

  The engineering feats are up there with anything, but it’s a next token predictor.

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