ialacol
open_llama
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ialacol | open_llama | |
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4 | 52 | |
138 | 7,193 | |
- | 1.3% | |
8.9 | 5.3 | |
3 months ago | 10 months ago | |
Python | ||
MIT License | Apache License 2.0 |
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ialacol
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Cloud Native Workflow for *Private* AI Apps
# This is the configuration file for DevSpace # # devspace use namespace private-ai # suggest to use a namespace instead of the default name space # devspace deploy # deploy the skeleton of the app and the dependencies (ialacol) # devspace dev # start syncing files to the container # devspace purge # to clean up version: v2beta1 deployments: # This are the manifest our private app deployment # The app will be in "sleep mode" after `devspace deploy`, and start when we start # syncing files to the container by `devspace dev` private-ai-app: helm: chart: # We are deploying the so-called Component Chart: https://devspace.sh/component-chart/docs name: component-chart repo: https://charts.devspace.sh values: containers: - image: ghcr.io/loft-sh/devspace-containers/python:3-alpine command: - "sleep" args: - "99999" service: ports: - port: 8000 labels: app.kubernetes.io/name: private-ai-app ialacol: helm: # the backend for the AI app, we are using ialacol https://github.com/chenhunghan/ialacol/ chart: name: ialacol repo: https://chenhunghan.github.io/ialacol # overriding values.yaml of ialacol helm chart values: replicas: 1 deployment: image: quay.io/chenhunghan/ialacol:latest env: # We are using MPT-30B, which is the most sophisticated model at the moment # If you want to start with some small but mightym try orca-mini # DEFAULT_MODEL_HG_REPO_ID: TheBloke/orca_mini_3B-GGML # DEFAULT_MODEL_FILE: orca-mini-3b.ggmlv3.q4_0.bin # MPT-30B DEFAULT_MODEL_HG_REPO_ID: TheBloke/mpt-30B-GGML DEFAULT_MODEL_FILE: mpt-30b.ggmlv0.q4_1.bin DEFAULT_MODEL_META: "" # Request more resource if needed resources: {} # pvc for storing the cache cache: persistence: size: 5Gi accessModes: - ReadWriteOnce storageClass: ~ cacheMountPath: /app/cache # pvc for storing the models model: persistence: size: 20Gi accessModes: - ReadWriteOnce storageClass: ~ modelMountPath: /app/models service: type: ClusterIP port: 8000 annotations: {} # You might want to use the following to select a node with more CPU and memory # for MPT-30B, we need at least 32GB of memory nodeSelector: {} tolerations: [] affinity: {}
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Offline AI π€ on Github Actions π
ββοΈπ°
You might be wondering why running Kubernetes is necessary for this project. This article was actually created during the development of a testing CI for the OSS project ialacol. The goal was to have a basic smoke test that verifies the Helm charts and ensures the endpoint returns a 200 status code. You can find the full source of the testing CI YAML here.
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Containerized AI before Apocalypse π³π€
We are deploying a Helm release orca-mini-3b using Helm chart ialacol
- Deploy private AI to cluster
open_llama
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How Open is Generative AI? Part 2
The RedPajama dataset was adapted by the OpenLLaMA project at UC Berkeley, creating an open-source LLaMA equivalent without Metaβs restrictions. The model's later version also included data from Falcon and StarCoder. This highlights the importance of open-source models and datasets, enabling free repurposing and innovation.
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GPT-4 API general availability
OpenLLaMA is though. https://github.com/openlm-research/open_llama
All of these are surmountable problems.
We can beat OpenAI.
We can drain their moat.
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Recommend me a computer for local a.i for 500 $
#1: π Open-source Reproduction of Meta AIβs LLaMA OpenLLaMA-13B released. (trained for 1T tokens) | 0 comments #2: π #1 on HuggingFace.co's Leaderboard Model Falcon 40B is now Free (Apache 2.0 License) | 0 comments #3: π Have you seen this repo? "running LLMs on consumer-grade hardware. compatible models: llama.cpp, alpaca.cpp, gpt4all.cpp, rwkv.cpp, whisper.cpp, vicuna, koala, gpt4all-j, cerebras and many others!" | 0 comments
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Who is openllama from?
Trained OpenLLaMA models are from the OpenLM Research team in collaboration with Stability AI: https://github.com/openlm-research/open_llama
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Personal GPT: A tiny AI Chatbot that runs fully offline on your iPhone
I can't use Llama or any model from the Llama family, due to license restrictions. Although now there's also the OpenLlama family of models, which have the same architecture but were trained on an open dataset (RedPajama, the same dataset the base model in my app was trained on). I'd love to pursue the direction of extended context lengths for on-device LLMs. Likely in a month or so, when I've implemented all the product feature that I currently have on my backlog.
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XGen-7B, a new 7B foundational model trained on up to 8K length for 1.5T tokens
https://github.com/openlm-research/open_llama#update-0615202...).
XGen-7B is probably the superior 7B model, it's trained on more tokens and a longer default sequence length (although both presumably can adopt SuperHOT (Position Interpolation) to extend context), but larger models still probably perform better on an absolute basis.
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MosaicML Agrees to Join Databricks to Power Generative AI for All
Compare it to openllama. It github doesn't have a single script on how to do anything.
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Databricks Strikes $1.3B Deal for Generative AI Startup MosaicML
OpenLLaMA models up to 13B parameters have now been trained on 1T tokens:
https://github.com/openlm-research/open_llama
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Containerized AI before Apocalypse π³π€
The deployed LLM binary, orca mini, has 3 billion parameters. Orca mini is based on the OpenLLaMA project.
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AI β weekly megathread!
OpenLM Research released its 1T token version of OpenLLaMA 13B - the permissively licensed open source reproduction of Meta AI's LLaMA large language model. [Details].
What are some alternatives?
langstream - LangStream. Event-Driven Developer Platform for Building and Running LLM AI Apps. Powered by Kubernetes and Kafka.
FastChat - An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.
dify - Dify is an open-source LLM app development platform. Dify's intuitive interface combines AI workflow, RAG pipeline, agent capabilities, model management, observability features and more, letting you quickly go from prototype to production.
llama.cpp - LLM inference in C/C++
Pontus - Open Source Privacy Layer
RWKV-LM - RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.
gpt4all - gpt4all: run open-source LLMs anywhere
gorilla - Gorilla: An API store for LLMs
ggml - Tensor library for machine learning
gpt-json - Structured and typehinted GPT responses in Python
koboldcpp - A simple one-file way to run various GGML and GGUF models with KoboldAI's UI
Open-Llama - The complete training code of the open-source high-performance Llama model, including the full process from pre-training to RLHF.