81–90 of 148 posts
The most interesting thing about this is the way it was trained using synthetic data, which is described in quite a bit of detail in the technical report: https://arxiv.org/abs/2412.08905 Microsoft haven't officially released the weights yet but there are unofficial GGUFs up on Hugging Face already. I tried this one: https://huggingface.co/matteogeniaccio/phi-4/tree/main I got it working with my LLM tool like this: l…
Phi is all about synthetic training and prompt -> svg -> render -> evaluate image -> feedback loop feels like ideal fit for synthetic learning.
You can push it quite far with stuff like basic 2d physics etc with plotting scene after N seconds or optics/rays, magnetic force etc.
SVG as LLM window to physical world.
I wonder what will be next month's buzzphrase.
Earlier quoted context omitted.
We’re already past that point! MacBooks can easily run models exceeding GPT-3.5, such as Llama 3.1 8B, Qwen 2.5 8B, or Gemma 2 9B. These models run at very comfortable speeds on Apple Silicon. And they are distinctly more capable and less prone to hallucination than GPT-3.5 was. Llama 3.3 70B and Qwen 2.5 72B are certainly comparable to GPT-4, and they will run on MacBook Pros with at least 64GB of RAM. However, I ha…
The coolness of local LLMs is THE only reason I am sadly eyeing upgrading from M1 64GB to M4/5 128+GB.
Earlier quoted context omitted.
Wow, those responses are better than I expected. Part of me was expecting terrible responses since Phi-3 was amazing on paper too but terrible in practice.
One of the funniest tech subplots in recent memory. TL;DR it was nigh-impossible to get it to emit the proper "end of message" token. (IMHO the chat training was too rushed). So all the local LLM apps tried silently hacking around it. The funny thing to me was no one would say it out loud. Field isn't very consumer friendly, yet.
I sometimes wonder how many other model or vendor-specific improvements there are, that are missed by third-party tools despite being well-documented by the vendors.
Earlier quoted context omitted.
One of the funniest tech subplots in recent memory. TL;DR it was nigh-impossible to get it to emit the proper "end of message" token. (IMHO the chat training was too rushed). So all the local LLM apps tried silently hacking around it. The funny thing to me was no one would say it out loud. Field isn't very consumer friendly, yet.
Speaking of, I wonder if and how many of the existing frontends, interfaces and support packages that generalize over multiple LLMs, and include Anthropic, actually know how to prompt it correctly. Seems like most developers missed the memo on https://docs.anthropic.com/en/docs/build-with-claude/prompt-... , and I regularly end up in situation in which I wish they gave more minute control on how the request is assemb…
You might be interested in Telosnex, been working on it for ~year and it's in good shape and is more or less designed for this sort of flexibility / allowing user input into requests. Pick any* provider, write up your own canned scripts, with incremental complexity: ex. your average user would just perceive it as "that AI app with the little picker for search vs. chat vs. art"
* OpenAI, Claude, Mistral, Groq Llama 3.x, and one I'm forgetting....Google! And .gguf
The worst was the gpt4o update in November. Basically a 2 liner on what it is better at and in reality it regressed in multiple benchmarks.
Here we just get MMLU, which is widely known to be saturated and knowing they trained on synthetic data, we have no idea how much "weight" was given to having MMLU like training data.
Benchmarks are not perfect, but they give me context to build upon. ---
edit: the benchmarks are covered in the paper: https://arxiv.org/pdf/2412.08905
The most interesting thing about this is the way it was trained using synthetic data, which is described in quite a bit of detail in the technical report: https://arxiv.org/abs/2412.08905 Microsoft haven't officially released the weights yet but there are unofficial GGUFs up on Hugging Face already. I tried this one: https://huggingface.co/matteogeniaccio/phi-4/tree/main I got it working with my LLM tool like this: l…
> Chain-of-Thought: Data should encourage systematic reasoning, teaching the model various approaches to the problems in a step-by-step manner.
The most interesting thing about this is the way it was trained using synthetic data, which is described in quite a bit of detail in the technical report: https://arxiv.org/abs/2412.08905 Microsoft haven't officially released the weights yet but there are unofficial GGUFs up on Hugging Face already. I tried this one: https://huggingface.co/matteogeniaccio/phi-4/tree/main I got it working with my LLM tool like this: l…
Is this not supposed to cause Model collapse?
So we moved from "reasoning" to "complex reasoning". I wonder what will be next month's buzzphrase.
Only from the perspective of those still complaining about the use of the term "reasoning", who now find themselves left behind as the world has moved on.
For everyone else, the phrasing change perfectly fits the technological change.