Bo

Bodhi App – Run Open Source/Weights HuggingFace LLMs Locally

Hacker News

Bodhi App – Run Open Source/Weights HuggingFace LLMs Locally

Hi HN, I'm excited to share Bodhi App, a tool designed to simplify running open-source Large Language Models (LLMs) locally on your laptops. While we currently support M2 Macs, we plan to support other platforms as our community grows. # Problem To use LLMs, you typically need to purchase a subscription from providers like OpenAI or Anthropic, or use OpenAI API credits with compatible Chat UIs. These options can not only burden you financially, but also raise data security and privacy concerns. Many laptops are capable of running powerful open-source LLMs, but for non-tech users, setting them up can be challenging. # Solution: Bodhi App Bodhi App allows you to run LLMs on your own hardware, ensuring data privacy and cost savings. Our goal is to bring the power of LLMs to everyone. Built with non-tech users in mind, Bodhi App ships with a simple Chat UI, making it easy to start conversing with an LLM. It also exposes OpenAI-compatible APIs, enabling other apps to use LLM services without relying on external providers. # Features Bodhi App currently supports: 1. Running GGUF format open-source LLMs from the Huggingface repository. 2. An in-built Chat UI to quickly start conversations with an LLM. 3. A powerful and familiar CLI to download and configure models from the Huggingface ecosystem. 4. Exposing LLMs as OpenAI-compatible APIs for use by other apps. -- # Feature Comparison: Bodhi App vs. Ollama ## Bodhi App - Targeted at non-tech users. - Includes a simple Chat UI to get started quickly. - Integrates well with the Huggingface ecosystem: - Use Huggingface repo/filename to run a model. - Use tokenizer_config.json for chat templates. - Currently only supports Mac M2. ## Ollama - Requires some technical insight. - No inbuilt Chat UI. - Requires baking the model using a custom process: - Modelfile - Golang template to specify chat templates. - Supports various OS platforms. Bodhi App leverages the Huggingface ecosystem, avoiding the need to reinvent the wheel with Modelfile etc., and making it easier to get started quickly. # Quickstart Try Bodhi App today by following these simple steps: ```bash brew tap BodhiSearch/apps brew install --cask bodhi bodhi run llama3:instruct ``` # Documentation and Tutorials - Technical docs on GitHub (README.md, docs folder): https://github.com/BodhiSearch/BodhiApp - YouTube playlist covering Bodhi App features in detail: https://www.youtube.com/playlist?list=PLavvg7KIktFI1ZaFc2nLe... # Conclusion We would love for HN to try out Bodhi App and provide feedback. You can reach us through: - Raising an issue on GitHub: https://github.com/BodhiSearch/BodhiApp - Connecting with the developer on Twitter: https://twitter.com/AmirNagri - Leaving a comment on our YouTube tutorials: https://www.youtube.com/playlist?list=PLavvg7KIktFI1ZaFc2nLe... Please show your support by starring the repo on Github. Thank you for your time! Best, The Bodhi Team

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, started, compatible · Missing: reddit linkedin, podcasting, created
98%98% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, apps · Missing: agents, macos, agent
90%90% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, open source, llama · Missing: https docs, just released, exist
72%72% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, users · Missing: mobile apps, ios, personal
33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr, subscription · Missing: mrr, revenue, profit
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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