vllm VS OpenPipe

I guess the root cause of my claim is that OpenAI won't tell us whether or not GPT-3.5 is an MoE model, and I assumed it wasn't. Since GPT-3.5 is clearly nondeterministic at temp=0, I believed the nondeterminism was due to FPU stuff, and this effect was amplified with GPT-4's MoE. But if GPT-3.5 is also MoE then that's just wrong.

What makes this especially tricky is that small models are truly 100% deterministic at temp=0 because the relative likelihoods are too coarse for FPU issues to be a factor. I had thought 3.5 was big enough that some of its token probabilities were too fine-grained for the FPU. But that's probably wrong.

On the other hand, it's not just GPT, there are currently floating-point difficulties in vllm which significantly affect the determinism of any model run on it: https://github.com/vllm-project/vllm/issues/966 Note that a suggested fix is upcasting to float32. So it's possible that GPT-3.5 is using an especially low-precision float and introducing nondeterminism by saving money on compute costs.

Sadly I do not have the money[1] to actually run a test to falsify any of this. It seems like this would be a good little research project.

[1] Or the time, or the motivation :) But this stuff is expensive.

The easiest is to use vllm (https://github.com/vllm-project/vllm) to run it on a Couple of A100's, and you can benchmark this using this library (https://github.com/EleutherAI/lm-evaluation-harness)

Great question! scheduling workloads onto GPUs in a way where VRAM is being utilised efficiently was quite the challenge.

What we found was the IO latency for loading model weights into VRAM will kill responsiveness if you don't "re-use" sessions (i.e. where the model weights remain loaded and you run multiple inference sessions over the same loaded weights).

Obviously projects like https://github.com/vllm-project/vllm exist but we needed to build out a scheduler that can run a fleet of GPUs for a matrix of text/image vs inference/finetune sessions.

disclaimer: I work on Helix

FYI, vLLM also just added experiment multi-lora support: https://github.com/vllm-project/vllm/releases/tag/v0.3.0

Also check out the new prefix caching, I see huge potential for batch processing purposes there!

- Fireworks: $0.20

If you're looking for an end-to-end flow that will help you gather the training data, validate it, run the fine tune and then define evaluations, you could also check out my company, OpenPipe (https://openpipe.ai/). In addition to hosting your model, we help you organize your training data, relabel if necessary, define evaluations on the finished fine-tune, and monitor its performance in production. Our inference prices are higher than the above providers, but once you're happy with your model you can always export your weights and host them on one of the above!

Btw, if you've tried fine-tuning OpenAI models before January and came away unimpressed with the quality of the finished model, it's worth trying again. They made some unannounced changes in the last few months that make the fine-tuned models much stronger.

That said, we've found that Mixtral fine-tunes still typically outperform GPT-3.5 fine tunes, and are far cheaper to serve. It's a bit of a plug, but I honestly think we have the simplest platform to fine-tune multiple models (both API-based like OpenAI as well as open source alternatives) side by side and compare quality. https://openpipe.ai

IMO it's possible to over-generalize from this datapoint (lol). While it's true that creating a general "finance" model that's stronger than GPT-4 is hard, training a task-specific model is much easier. Eg. "a model that's better than GPT-4 at answering finance-related questions": very hard. "A model that's better than GPT-4 at extracting forward-looking financial projections in a standard format": very easy.

And in practice, most tasks people are using GPT-4 for in production are more like the latter than the former.

(Disclaimer: building https://openpipe.ai, which makes it super easy to productize this workflow).

In this article I'll share how I used OpenPipe to effortlessly fine tune Mistral 7B, reducing the cost of one of my prompts by 50x. I included tips and recommendations if you are doing this for the first time, because I definitely left some performance increases on the table. Skip to Fine Tuning Open Recommender if you are specifically interested in what the fine tuning process looks like. You can always DM me on Twitter (@experilearning) or leave a comment if you have questions!

The problem is that most non-OpenAI models haven't actually been fine-tuned with function calling in mind, and getting a model to output function-calling-like syntax without having been trained on it is quite unreliable. There are a few alternatives that have been (OpenHermes 2.5 has some function calling in its dataset and does a decent job with it, and the latest Claude does as well), but for now it just doesn't work great.

That said, it's not that hard to fine-tune a model to understand function calling -- we do that as part of all of our OpenPipe fine tunes, and you can see the serialization method we use here: https://github.com/OpenPipe/OpenPipe/blob/main/app/src/model...

It isn't particularly difficult, and I'd expect more general-purpose fine-tunes will start doing something similar as they get more mature!

Eh, OpenAI is too cheap to beat at their own game.

But there are a ton of use-cases where a 1 to 7B parameter fine-tuned model will be faster, cheaper and easier to deploy than a prompted or fine-tuned GPT-3.5-sized model.

In fact, it might be a strong statement but I'd argue that most current use-cases for (non-fine-tuned) GPT-3.5 fit in that bucket.

(Disclaimer: currently building https://openpipe.ai; making it trivial for product engineers to replace OpenAI prompts with their own fine-tuned models.)

Yep! The linked notebook includes an example of exactly that (fine-tuning a 7b model to match the syntax of GPT-4 function call responses): https://github.com/OpenPipe/OpenPipe/blob/main/examples/clas...

Hey HN! I'm working on OpenPipe, an open source prompt workshop. I wanted to share a feature we recently released: prompt translations. Prompt translations allow you to quickly convert a prompt between GPT 3.5, Llama 2, and Claude 1/2 compatible formats. The common case would be if you’re using GPT 3.5 in production and are interested in evaluating a Claude or Llama 2 model for your use case. Here's a screen recording to show how it works in our UI: https://twitter.com/OpenPipeLab/status/1687875354311180288

We’ve found a lot of our users are interested in evaluating Claude or Llama 2, but weren’t sure what changes they need to make to their prompts to get the best performance out of those models. Prompt translations make that easier.

A bit more background: OpenPipe is an open-source prompt studio that lets you test your LLM prompts against scenarios from your real workloads. We currently support GPT 3.5/4, Claude 1/2, and Llama 2. The full codebase (including prompt translations) is available at https://github.com/OpenPipe/OpenPipe. If you’d prefer a managed experience, you can also sign up for our hosted version at at https://openpipe.ai/.

Happy to answer any questions!

Star our Github repo at https://github.com/openpipe/openpipe

This is fantastic! I found myself nodding along in many places. I've definitely found in practice that evals are critical to shipping LLM-based apps with confidence. I'm actually working on an open-source tool in this space: https://github.com/openpipe/openpipe. Would love any feedback on ways to make it more useful. :)