Run the large Hy3 language model locally with correct chat formatting.
Split a huge model across multiple GPUs to fit it in memory.
Speed up text generation using built-in speculative decoding.
Offload conversation memory to system RAM for very long contexts.
| noonr48/ik_llama-hy3 | achanana/mavsdk | alange/llama.cpp | |
|---|---|---|---|
| Stars | 0 | — | 0 |
| Language | C++ | C++ | C++ |
| Last pushed | — | 2024-05-20 | — |
| Maintenance | — | Dormant | — |
| Setup difficulty | hard | moderate | moderate |
| Complexity | 5/5 | 4/5 | 4/5 |
| Audience | developer | developer | developer |
Figures from each repo's GitHub metadata at analysis time.
Requires building from source with CUDA and roughly 158GB of GPU or system memory for the model.
This project is a fork of a program called ik_llama.cpp, modified to run a specific AI language model named Tencent Hy3. Hy3 is a very large model, about 295 billion parameters, but it only uses a small active portion of itself for each response, which is a design called mixture of experts. The fork adds the code needed to load this particular model correctly, understand its custom formatting, and run it efficiently. The main audience is people who already run large language models on their own hardware and want to try this specific model. It is not a beginner tool. Running Hy3 requires downloading a huge quantized version of the model, around 158 gigabytes, and enough GPU memory to hold it, often spread across multiple graphics cards. The README gives exact build and startup commands, including flags for splitting the model across GPUs, offloading memory to system RAM for longer conversations, and turning on a speed feature called speculative decoding that lets the model draft and check several words at once. The project also fixes a few technical bugs specific to this model, such as an incorrect token that marks the end of a response and a custom template file needed so chat messages are formatted the way the model expects. The author lists clearly what has been tested and confirmed working, like model loading, text generation, multi GPU setups, and the speed up feature, as well as what has not been tested yet, such as running on a single GPU or using other compression formats. Overall this is a specialized, technical tool for people already comfortable building AI inference software from source code and managing serious GPU hardware. It builds directly on top of an existing open source project rather than starting from scratch, and the README documents the exact commits and files changed compared to the original version.
A fork of ik_llama.cpp that adds support for running Tencent's huge Hy3 AI language model on your own GPUs.
Mainly C++. The stack also includes C++, CUDA, CMake.
Setup difficulty is rated hard, with roughly 1day+ to a first successful run.
Mainly developer.
This repo across BitVibe Labs
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