4 ms·
Who is using 7Bs in a serious manner, instead of OpenAI, in a cost efficient way?
by ramoz 3y ago
Who is using 7Bs in a serious manner, instead of OpenAI, in a cost efficient way?
- justinl33 3y ago1. fine tune to specific tasks 2. Not subject to OpenAI’s censorshi 3. Can run on local instead of cloud compute (offline) 4. experimentation
- ramoz 3y agoNot sure what you’re responding to
- dingdingdang 3y agoUsing Mistral ft optimized 1218 7b q5km - very useful for basic queries/creative input. Often just as useful as ChatGPT and feels far more "real" to have it fully local, don't want to depend fully on one proprietary service for something as fundamentally useful as this!
- ukuina 3y agoI have spent more time than I would like to admit trying to get 3B-34B models working for "serious" usecases like RAG, code comprehension/generation and text summarization, across dozens of "leading" free LLMs, and none of them (not even Mixtral MoE or its derivatives) come close to gpt-3.5-turbo for consistency and depth, and there is no equal for gpt-4(-turbo). LLM Leaderboards and benchmarks do not show the complete picture for YOUR specific usecase.
- dcreater 3y agoIf you can post results that would be extremely extremely helpful. This is what I've been looking for -real world performance - rather than gamed leaderboards
- ukuina 3y agoThat's just it: It is highly dependent on your inputs, expectations and usecase. My observations below. Mixtral does about average at summarization. Not enough details are presented in the summary. Passable for non-decisionbound workflows. Using quantized models injects occasional spelling errors, so fall back on Mistral unquantized. Skip all the Mistral finetunes. Only choose Instruct models. There are no good local code comprehension/generation models (and that includes CodeLlama, DeepSeek, Starcoder, WizardCoder, Phind). You will spend as much time guiding/rerolling/correcting output as coding it yourself, assuming your task is nontrivial and exceeds 100+ LoC. Code completion via continue.dev using local models did not yield good results, either. No local models are good at agentic tasks (e.g., via AutoGen). You might have better luck rolling your own with function-calling via NexusRaven2 than using Agent frameworks with local LLMs. Claude-Instant edges out gpt-3.5-turbo for longform summarization with its 100k context. It is what I use for https://HackYourNews.com https://HackYourNews.com because chunked/rolling summarization is noisy. For every non-hobby task, I am switching to gpt-4-*, choosing between base, 32k, and turbo depending on speed/correctness/cost/length tradeoffs. They are not perfect, but there is no competition.
- m_kos 3y agoWould you mind sharing how you got access to Claude? I applied a long time ago and have never heard back from them.
- dcreater 3y agothats really useful insight. Yes it is usecase specific but thats also why its crucial for real world trial results to be shared so that theres a better understanding thats qualitatively accurate. P.S: hackyournews.com is great!!
- ramoz 3y agoAnd this is my drawn out experience as well
- reexpressionist 3y agoIf the end goal is document classification and/or semantic search, the Reexpress Fast I model (3.2 billion parameters) is a good choice. The key is that it produces reliable uncertainty estimates (for classification), so you know if you need a larger (or alternative) model. (In fact, an argument can be made that since the other models don't produce such uncertainty estimates, they are not ideal for serious use cases without adding an additional mechanism, such as ensembling with the Reexpress model.)