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RestGPT
- salamo 3y agoIt's actually really interesting to see GOFAI techniques like planning used in conjunction with LLMs.
- cscurmudgeon 3y agoI see a GOFAI resurgence thanks to LLMs.
- deleted 3y ago[deleted]
- albert_e 3y agoChatGPT + Noteable is already powerful to get some work done via API calls (after installing and importing the libraries, writing Python code, managing secrets for authentication etc) There is surely scope to streamline this much further I am very intently watching this space
- thelittleone 3y agoInterested to learn more (big fan of data stories). Do you have any particular use cases you would recommend to look into?
- pplonski86 3y agoI've seen Noteable+ChatGPT demo, where user can chat with ChatGPT and responses where executed in the Noteable-hosted Python notebook. It was cool! It would be also cool to have such plugin for Google Colab. I hope someone will come with a new way to interact with LLM models other than chat UI. It would make code writing even more faster.
- behnamoh 3y agoIt seems after 1-2 years that the true power of LLMs is in DevOps. I got pretty excited when I tried GPT-3 (completion model), but as time went by and OpenAI shifted to chat models, we lost control over the LLM part and found new meaning in taking whatever model OpenAI made available as a blackbox and "chained" it to other tools we already had, like data bases, APIs, function calls/tools, etc. I'd say DevOps is exactly where open source is seriously behind; there are decent open source models but it costs so much to self host them, despite the full power and control we have on them (via text generation webui and the like). OpenAI is playing the DevOps game (starting maybe with introduction of ChatML). Open source community plays the LLM and benchmarks game. Ironically, the two are converging, meaning that OpenAI's models are getting dumber (not the API) thanks to censorship and RLHF, to the point that open source models are even better than some OpenAI models in some aspects. On the other hand, open source models are getting better tooling and DevOps thanks to oobabooga, llama.cpp, etc. I'm seriously waiting for competitors to change nVidia's monopoly in this space. Maybe Apple?
- antupis 3y agoI think currently M2 max is best bang for buck running interface in open source model. But use case is so niche that Apple probably doesn't actively start supporting open source models. In the long run I hope some smaller company gets shit together and starts competing with NVIDIA.
- behnamoh 3y agoAt $6,000, how is M2 Max the best bang for the buck?! One could get two used 3090s and setup a decent PC at lower prices.
- rankun203 3y agoThe GPU support in ML frameworks however is really not impressive. I have a Macbook with M1 Max 64G RAM, I can load a 7b model for fine-tuning (Huggingface Trainer, Pytorch, MPS), but the speed is just too slow, can only reach to 50% the speed of an i5-12500 CPU in my tests.
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- transcriptase 3y agoPaging the Google Home team…
- deleted 3y ago[deleted]
- Obscurity4340 3y agoIs there any local GPT model available, even if its a bit older? I had heard maybe 3.5 leaked but perhaps I'm mixing up StableDiffusion?
- impulser_ 3y agoThe examples are pretty lame since you can do what the examples do way faster without using a LLM and paying OpenAI.
- venky180 3y agoHow does this compare to agentGPT?
- airstrike 3y agoFinally a use for my IRC bot
- cosbgn 3y agoThis is interesting, I do something similar with unfetch.com - I have some examples on unfetch.com/directory - There are a lot of potential use cases for LLm & APIs
- pplonski86 3y agoThe service unfetch.com looks cool. Can I run it locally on my machine, not in the cloud? Is it open-source?
- cosbgn 3y agoIt's not opensouce, even though it's something I would like to be able to do soon. We offer enterprise plans, which use LLAMA2 instead of openai and can run on your own cloud (or even locally if you have enough RAM). If you are interested send me an email at c@unfetch.com
- stevage 3y ago> WARNING: this will remove all your data from spotify! That is quite the caveat.
- seanthemon 3y agoI feel like that script needs a few 'are you completely sure?' Prompts
- Towaway69 3y agothanks for pointing that out!
- two_in_one 3y agoThe problem here is where these actions come from. Generic LLM cannot generate correct actions in many (if not most) real life cases. So, it will have to learn, and LLMs aren't good at learning. For example: "I'm tired, play my favorite". The action depends on _who_ is saying and on what's going on right now. There may be someone sleeping, or watching TV. I'm afraid that acceptable solution is much more complicated.
- pmx 3y agoDO we have to expect _that_ level of understanding from the agent, though? If my wife said that to me, I may have a good chance of queuing up the song she has in mind, but anyone else? No chance. I don't expect tools like this to be able to understand cryptic requests and always come to the right answer. I'm happy if I can request a song or an action, or anything else in the same way i might ask another human who doesn't know me intimately.
- swexbe 3y agoIf not, how is this more useful than something like Siri?
- famouswaffles 3y agoNatural language understanding. Siri doesn't get context at all. You can twist unstructed data or requests however you like and the LLM will deal with it just fine. "Play my favorite" is just a knowledge problem. If GPT fails there, it's because it doesn't know your favorite, not because it can't parse the request or understand what you need it to do. You have to speak certain ways to Siri to get it to do things. Unless specifically hard-coded, Siri will never receive "damn I'm finding it hard to read" as input as decide to turn on the lights. GPT will.
- troupo 3y ago"Siri doesn't get context at all." and yet immediately "GPT fails there, it's because it doesn't know your favorite" "Knowing your favorite" is the context. > Unless specifically hard-coded, Siri will never receive "damn I'm finding it hard to read" as input as decide to turn on the lights. GPT will. Of course it won't. You have to very specifically fine tune it to understand what light conditions are, where you are in the house, and what it is you need to turn on.
- pplonski86 3y agoHow RestGPT differs from ToolLLM or Gorilla? papers: 1. ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs https://arxiv.org/abs/2307.16789 https://arxiv.org/abs/2307.16789 2. Gorilla: Large Language Model Connected with Massive APIs https://arxiv.org/abs/2305.15334 https://arxiv.org/abs/2305.15334
- mellosouls 3y agoDid you open the linked paper in the OP? Gorilla is compared; ToolLLM seems to postdate this project.
- amelius 3y agoWaiting for BashGPT. It could use curl to do REST calls too, and a lot more.
- pplonski86 3y agohttps://github.com/KillianLucas/open-interpreter https://github.com/KillianLucas/open-interpreter
- westurner 3y ago"Gorilla: Large Language Model Connected with Massive APIs" (2023) https://gorilla.cs.berkeley.edu/ https://gorilla.cs.berkeley.edu/ : > Gorilla enables LLMs to use tools by invoking APIs. Given a natural language query, Gorilla comes up with the semantically- and syntactically- correct API to invoke. With Gorilla, we are the first to demonstrate how to use LLMs to invoke 1,600+ (and growing) API calls accurately while reducing hallucination. We also release APIBench, the largest collection of APIs, curated and easy to be trained on! Join us, as we try to expand the largest API store and teach LLMs how to write them! eval/: https://github.com/ShishirPatil/gorilla/tree/main/eval https://github.com/ShishirPatil/gorilla/tree/main/eval - "Gorilla: Large Language Model connected with massive APIs" (2023-05) https://news.ycombinator.com/item?id=36073241 https://news.ycombinator.com/item?id=36073241 - "Gorilla: Large Language Model Connected with APIs" (2023-06) https://news.ycombinator.com/item?id=36333290 https://news.ycombinator.com/item?id=36333290 - "Gorilla-CLI: LLMs for CLI including K8s/AWS/GCP/Azure/sed and 1500 APIs (github.com/gorilla-llm)" (2023-06) https://news.ycombinator.com/item?id=36524078 https://news.ycombinator.com/item?id=36524078
- mjan22640 3y agoSeparating the language model from the knowledge model (and then splitting that into domain specific knowledge models) is the next low(ish) hanging progression step in the field.
- treprinum 3y agoDoes this use a chain of LLM/REST requests or does it use a single multi-task LLM request that launches a REST call while populating its output?