mify
text-generation-webui
mify | text-generation-webui | |
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8 | 876 | |
127 | 37,023 | |
2.4% | - | |
7.3 | 9.9 | |
10 days ago | 5 days ago | |
Go | Python | |
Apache License 2.0 | GNU Affero General Public License v3.0 |
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mify
- Mify – CLI that generates and maintains your backend infra code
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We migrated our back end from Vercel to Fly.io and the challenges we faced
This! Can't agree more, I think we share the same idea, that's what the tool we're making is about: https://github.com/mify-io/mify/. It generates backend service code in a scalable way from the beginning, so that you wouldn't have to rewrite and move services to some other platform.
It's better to have good architecture from the beginning, but I understand why people choose these platforms - they are saving a lot of time in the initial development, that helps them iterate quickly. What will happen next is that people spending time and resources to perform costly migrations, and some do this more that once.
- Show HN: Mify – CLI that generates and maintains your back end infra code
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Micro services share schemas and models
You can try switching to OpenAPI schemas, it's similar to pydantic schemas, and you can generate Go service with types based on it, we're building an open source tool to help with that and we support both Go and Python, check it out here you may find it helpful.
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What do you use generated code or generic code?
Code generation FTW, I think there is a lot of friction with gluing stuff together manually, especially on the backend side, we are building an open-source code generation tool to cover things like APIs with structured logging and metrics, configuration, and authentication, check it out: https://github.com/mify-io/mify
- Show HN: Mify – CLI tool for generating cloud app structure
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Any Working Example for Swagger integartion with golang?
Yes, Swagger/OpenAPI is rarely works out of the box, in Mify (https://github.com/mify-io/mify) we did a lot of work to integrate it and still we have much stuff to improve, but you can try it, I think it works pretty smoothly.
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Trying to get started with my own product - overwhelmed with technical decisions
I do believe, though, that it better to start from something you're most familiar with, and right now we only support Go, Python and React, so this may not be for you, but check it if you're interested: https://github.com/mify-io/mify
text-generation-webui
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Ask HN: What is the current (Apr. 2024) gold standard of running an LLM locally?
Some of the tools offer a path to doing tool use (fetching URLs and doing things with them) or RAG (searching your documents). I think Oobabooga https://github.com/oobabooga/text-generation-webui offers the latter through plugins.
Our tool, https://github.com/transformerlab/transformerlab-app also supports the latter (document search) using local llms.
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Ask HN: How to get started with local language models?
You can use webui https://github.com/oobabooga/text-generation-webui
Once you get a version up and running I make a copy before I update it as several times updates have broken my working version and caused headaches.
a decent explanation of parameters outside of reading archive papers: https://github.com/oobabooga/text-generation-webui/wiki/03-%...
a news ai website:
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text-generation-webui VS LibreChat - a user suggested alternative
2 projects | 29 Feb 2024
- Show HN: I made an app to use local AI as daily driver
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Ask HN: People who switched from GPT to their own models. How was it?
The other answers are recommending paths which give you #1. less control and #2. projects with smaller eco-systems.
If you want a truly general purpose front-end for LLMs, the only good solution right now is oobabooga: https://github.com/oobabooga/text-generation-webui
All other alternatives have only small fractions of the features that oobabooga supports. All other alternatives only support a fraction of the LLM backends that oobabooga supports, etc.
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AI Girlfriend Is a Data-Harvesting Horror Show
The example waifu in text-generation-webui is good enough for me.
https://github.com/oobabooga/text-generation-webui/blob/main...
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Nvidia's Chat with RTX is a promising AI chatbot that runs locally on your PC
> Downloading text-generation-webui takes a minute, let's you use any model and get going.
What you're missing here is you're already in this area deep enough to know what ooogoababagababa text-generation-webui is. Let's back out to the "average Windows desktop user" level. Assuming they even know how to find it:
1) Go to https://github.com/oobabooga/text-generation-webui?tab=readm...
2) See a bunch of instructions opening a terminal window and running random batch/powershell scripts. Powershell, etc will likely prompt you with a scary warning. Then you start wondering who ooobabagagagaba is...
3) Assuming you get this far (many users won't even get to step 1) you're greeted with a web interface[0] FILLED to the brim with technical jargon and extremely overwhelming options just to get a model loaded, which is another mind warp because you get to try to select between a bunch of random models with no clear meaning and non-sensical/joke sounding names from someone called "TheBloke". Ok...
Let's say you somehow braved this gauntlet and get this far now you get to chat with it. Ok, what about my local documents? text-generation-webui itself has nothing for that. Repeat this process over the 10 random open source projects from a bunch of names you've never heard of in an attempt to accomplish that.
This is "I saw this thing from Nvidia explode all over media, twitter, youtube, etc. I downloaded it from Nvidia, double-clicked, pointed it at a folder with documents, and it works".
That's the difference and it's very significant.
[0] - https://raw.githubusercontent.com/oobabooga/screenshots/main...
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Ask HN: What are your top 3 coolest software engineering tools?
Maybe a copout answer, but setting up a local LLM on my development machine has been invaluable. I use Deep Seek Coder 6.7 [0] and Oobabooga's UI [1]. It helps me solve simple problems and find bugs, while still leaving the larger architecture decisions to me.
[0] https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instr...
[1] https://github.com/oobabooga/text-generation-webui
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Meta AI releases Code Llama 70B
You can download it and run it with [this](https://github.com/oobabooga/text-generation-webui). There's an API mode that you could leverage from your VS Code extension.
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Ollama Python and JavaScript Libraries
Same question here. Ollama is fantastic as it makes it very easy to run models locally, But if you already have a lot of code that processes OpenAI API responses (with retry, streaming, async, caching etc), it would be nice to be able to simply switch the API client to Ollama, without having to have a whole other branch of code that handles Alama API responses. One way to do an easy switch is using the litellm library as a go-between but it’s not ideal (and I also recently found issues with their chat formatting for mistral models).
For an OpenAI compatible API my current favorite method is to spin up models using oobabooga TGW. Your OpenAI API code then works seamlessly by simply switching out the api_base to the ooba endpoint. Regarding chat formatting, even ooba’s Mistral formatting has issues[1] so I am doing my own in Langroid using HuggingFace tokenizer.apply_chat_template [2]
[1] https://github.com/oobabooga/text-generation-webui/issues/53...
[2] https://github.com/langroid/langroid/blob/main/langroid/lang...
Related question - I assume ollama auto detects and applies the right chat formatting template for a model?
What are some alternatives?
go-clean-template - Clean Architecture template for Golang services
KoboldAI - KoboldAI is generative AI software optimized for fictional use, but capable of much more!
golug - GoLug Presentation Code
llama.cpp - LLM inference in C/C++
go-rest - crud rest api template using gin framework, gin-swagger, gorm, godotenv
gpt4all - gpt4all: run open-source LLMs anywhere
TavernAI - Atmospheric adventure chat for AI language models (KoboldAI, NovelAI, Pygmalion, OpenAI chatgpt, gpt-4)
KoboldAI-Client
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
alpaca-lora - Instruct-tune LLaMA on consumer hardware
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]