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Top 23 Python llm Projects
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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api-for-open-llm
Openai style api for open large language models, using LLMs just as chatgpt! Support for LLaMA, LLaMA-2, BLOOM, Falcon, Baichuan, Qwen, Xverse, SqlCoder, CodeLLaMA, ChatGLM, ChatGLM2, ChatGLM3 etc. 开源大模型的统一后端接口
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DemoGPT
Create 🦜️🔗 LangChain apps by just using prompts🌟 Star to support our work! | 只需使用句子即可创建 LangChain 应用程序。 给个star支持我们的工作吧!
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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swirl-search
Swirl is an open-source search platform that uses AI to search multiple content and data sources simultaneously and return AI-ranked results. And provides summaries of your answers from searches using LLMs. It's a one-click, easy-to-use Retrieval Augmented Generation (RAG) Solution.
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safe-rlhf
Safe RLHF: Constrained Value Alignment via Safe Reinforcement Learning from Human Feedback
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dstack
dstack is an open-source orchestration engine for running AI workloads at scale in any cloud or data center. https://discord.gg/u8SmfwPpMd
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agenta
The all-in-one LLM developer platform: prompt management, evaluation, human feedback, and deployment all in one place.
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distilabel
⚗️ distilabel is a framework for synthetic data and AI feedback for AI engineers that require high-quality outputs, full data ownership, and overall efficiency.
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agent-protocol
Common interface for interacting with AI agents. The protocol is tech stack agnostic - you can use it with any framework for building agents.
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vectordb
A minimal Python package for storing and retrieving text using chunking, embeddings, and vector search. (by kagisearch)
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
Depends what model you want to train, and how well you want your computer to keep working while you're doing it.
If you're interested in large language models there's a table of vram requirements for fine-tuning at [1] which says you could do the most basic type of fine-tuning on a 7B parameter model with 8GB VRAM.
You'll find that training takes quite a long time, and as a lot of the GPU power is going on training, your computer's responsiveness will suffer - even basic things like scrolling in your web browser or changing tabs uses the GPU, after all.
Spend a bit more and you'll probably have a better time.
[1] https://github.com/hiyouga/LLaMA-Factory?tab=readme-ov-file#...
Package installer for Python (pip), we use this for installing the Python-based packages, such as Jupyter Lab, and we're going to use this for installing other Python-based tools like the Chroma DB vector database
Here’s another one - it’s older but has some interesting charts and graphs.
https://arxiv.org/abs/2303.18223
Project mention: Should I tell my employer about a product that makes my job irrelevant? | /r/cscareerquestions | 2023-07-11Is this of any help?
Project mention: GitHub - swirlai/swirl-search: Swirl is an open-source search platform that uses AI to search multiple content and data sources simultaneously, finds the best results using a reader LLM, then prompts Generative AI, enabling you to get answers based on your data. | /r/programming | 2023-12-05
Project mention: OpenAI: Streaming is now available in the Assistants API | news.ycombinator.com | 2024-03-14This was indeed true in the beginning, and I don’t know if this has changed. Inserting messages with Assistant role is crucial for many reasons, such as if you want to implement caching, or otherwise edit/compress a previous assistant response for cost or other reason.
At the time I implemented a work-around in Langroid[1]: since you can only insert a “user” role message, prepend the content with ASSISTANT: whenever you want it to be treated as an assistant role. This actually works as expected and I was able to do caching. I explained it in this forum:
https://community.openai.com/t/add-custom-roles-to-messages-...
[1] the Langroid code that adds a message with a given role, using this above “assistant spoofing trick”:
https://github.com/langroid/langroid/blob/main/langroid/agen...
So we thought it would be a good idea to create a framework that makes use of LoopGPT agent's memory and custom tooling capabilities. Let's jump right into the new features of this framework.
Coframe
Project mention: [R] Meet Beaver-7B: a Constrained Value-Aligned LLM via Safe RLHF Technique | /r/MachineLearning | 2023-05-16
We have recently added support to query data from SingleStore to our agent framework, LLMStack (https://github.com/trypromptly/LLMStack). Out of the box performance performance when prompting with just the table schemas is pretty good with GPT-4.
The more domain specific knowledge needed for queries, the harder it has gotten in general. We've had good success `teaching` the model different concepts in relation to the dataset and giving it example questions and queries greatly improved performance.
Project mention: Ask HN: How does deploying a fine-tuned model work | news.ycombinator.com | 2024-04-23You can use https://github.com/dstackai/dstack to deploy your model to the most affordable GPU clouds. It supports auto-scaling and other features.
Disclaimer: I’m the creator of dstack.
Project mention: Ask HN: How are you testing your LLM applications? | news.ycombinator.com | 2024-02-06I am biased, but I would use a platform and not roll your own solution. You will tend to underestimate the depth of capabilities needed for an eval framework.
Now for solutions, shameless plug here, we are building an open-source platform for experimenting and evaluating complex LLM apps (https://github.com/agenta-ai/agenta). We offer automatic evaluators as well as human annotation capabilities. Currently, we only provide testing before deployment, but we have plans to include post-production evaluations as well.
Other tools I would look at in the space are promptfoo (also open-source, more dev oriented), humanloop (one of the most feature complete tools in the space, enterprise oriented), however more enterprise oriented / costly) and vellum (YC company, more focused towards product teams)
Project mention: Open-source AI Feedback framework for scalable LLM Alignment | news.ycombinator.com | 2023-11-23
Project mention: Show HN: Common protocol for communication with (and between) AI Agents | news.ycombinator.com | 2023-08-09
Project mention: Show HN: LLMFlows – LangChain alternative for explicit and transparent apps | news.ycombinator.com | 2023-07-29
Check it out here
We needed a low latency, on premise solution that we can run on edge nodes (so lightweight) with sane defaults that anyone in the team can whim in a sec.
Result is this and we constantly benchmark performance of different embeddings to ensure best defaults.
[1] https://github.com/kagisearch/vectordb#embeddings-performanc...
Python llms related posts
- Large language models (e.g., ChatGPT) as research assistants
- LLM Is a Capable Regressor When Given In-Context Examples
- Show HN: Burr: An OS Framework for Building and Debugging GenAI Apps Faster
- Long-form factuality in large language models
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text-generation-webui VS LibreChat - a user suggested alternative
2 projects | 29 Feb 2024
- Validating the RAG Performance of Amazon Titan vs. Cohere Using Amazon Bedrock
- Ask HN: What have you built with LLMs?
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A note from our sponsor - InfluxDB
www.influxdata.com | 27 Apr 2024
Index
What are some of the best open-source llm projects in Python? This list will help you:
Project | Stars | |
---|---|---|
1 | LLaMA-Factory | 17,050 |
2 | chroma | 12,189 |
3 | LLMSurvey | 8,716 |
4 | AutoGPTQ | 3,744 |
5 | gpt-code-ui | 3,482 |
6 | api-for-open-llm | 1,952 |
7 | DemoGPT | 1,566 |
8 | swirl-search | 1,509 |
9 | langroid | 1,509 |
10 | loopgpt | 1,390 |
11 | coffee | 1,341 |
12 | safe-rlhf | 1,149 |
13 | LLMStack | 1,089 |
14 | dstack | 1,087 |
15 | LLMCompiler | 1,056 |
16 | llm_agents | 877 |
17 | agenta | 823 |
18 | distilabel | 825 |
19 | agent-protocol | 754 |
20 | DataDreamer | 632 |
21 | llmflows | 615 |
22 | oterm | 554 |
23 | vectordb | 543 |
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