LL

LLM, a Rust Crate/CLI for CPU Inference of LLMs (LLaMA, GPT-NeoX, etc.)

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LLM, a Rust Crate/CLI for CPU Inference of LLMs (LLaMA, GPT-NeoX, etc.)

G'day, HN! I'm one of the maintainers of `llm`. I've been working alongside a trusty group of contributors to bring this project to life, and we're now at a point where we're ready to share it with the world. Large language models (LLMs) are taking the computing world by storm due to their emergent abilities that allow them to perform a wide variety of tasks, including translation, summarization, code generation, and even some degree of reasoning. However, the ecosystem around LLMs is still in its infancy, and it can be difficult to get started with these models. `llm` is a one-stop shop for running inference on large language models (of the kind that power ChatGPT and more); we provide a CLI and a Rust crate for running inference on these models, all entirely open-source. The crate can be embedded in your own projects, allowing you to easily integrate LLMs into your own applications. We hope that `llm` can help to alleviate some of the pain points that users face when working with LLMs. Our goal is to build a robust solution for inferencing on LLMs that users can rely on for their projects, so that we can provide a moment of peace in the chaos of the LLM ecosystem. At present, we are powered by `ggml` (similar to `llama.cpp`), but we intend to add additional backends in the near-future. This means that we currently only support CPU inference, but we have several ideas in mind for how to add GPU support, as well as other accelerators. We're looking for feedback on the project, and we'd love to hear from you! If you're interested in contributing, please reach out to us on our Discord ( https://discord.gg/YB9WaXYAWU ), or post an issue on our GitHub ( https://github.com/rustformers/llm/issues ).

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45points
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, models · Missing: mac, agents, macos
91%91% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: started, including · Missing: supports, reddit linkedin, podcasting
79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: llama, ide, io · Missing: https docs, excited, just released
78%78% 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: users · Missing: plus, platform, intuitive
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: users · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
11%11% 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.

Correct prediction on native model

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