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docs : some fixes

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Georgi Gerganov 2024-04-09 18:50:55 +03:00
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docs/HOWTO-add-model.md
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# HOW TO
## Add a new model architecture to `llama.cpp` ## Add a new model architecture to `llama.cpp`
LLaMA C++ is built on top of [ggml](https://github.com/ggerganov/ggml) Tensor library for machine learning.
Model are stored in [GGUF](https://github.com/ggerganov/ggml/blob/master/docs/gguf.md).
#### Quick start
Adding a model requires few steps: Adding a model requires few steps:
1. Convert the model to GGUF 1. Convert the model to GGUF
@ -23,10 +16,10 @@ Also, it is important to check that the examples and main ggml backends (CUDA, M
### 1. Convert the model to GGUF ### 1. Convert the model to GGUF
This step is done in python with a `convert` script using [gguf-writer](https://pypi.org/project/gguf/) library. This step is done in python with a `convert` script using the [gguf](https://pypi.org/project/gguf/) library.
Depending on the model architecture, you can use either [convert.py](../convert.py) or [convert-hf-to-gguf.py](../convert-hf-to-gguf.py). Depending on the model architecture, you can use either [convert.py](../convert.py) or [convert-hf-to-gguf.py](../convert-hf-to-gguf.py).
The convert script reads the model configuration, tokenizer, tensor names+data and convert them to GGUF Metadata and tensors. The convert script reads the model configuration, tokenizer, tensor names+data and converts them to GGUF metadata and tensors.
The required steps to implement for an HF model are: The required steps to implement for an HF model are:
@ -105,20 +98,14 @@ This is the funniest part, you have to provide the inference graph implementatio
Have a look to existing implementation like `build_llama`, `build_dbrx` or `build_bert`. Have a look to existing implementation like `build_llama`, `build_dbrx` or `build_bert`.
When implementing a new graph, please note that the underlying `ggml` backends do not support them all, support of missing backend operations can be added in another PR. When implementing a new graph, please note that the underlying `ggml` backends might not support them all, support of missing backend operations can be added in another PR.
## Terminology ## GGUF specification
| term | description | link | https://github.com/ggerganov/ggml/blob/master/docs/gguf.md
|---------|---------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------|
| GGML | Georgi Gerganov Model Language a.k.a GPT-Generated Model Language | https://github.com/ggerganov/ggml |
| GGUF | GGML Universal File a.k.a GPT-Generated Unified Format, successor to GGML format, GGUF’s creation aligns with the needs of large-scale AI models. | https://github.com/ggerganov/ggml/blob/master/docs/gguf.md |
## Resources ## Resources
- [GGML - Large Language Models for Everyone](https://github.com/rustformers/llm/blob/main/crates/ggml/README.md): a
description of the GGML format provided by the maintainers of the `llm` Rust crate, which provides Rust bindings for
GGML
- YaRN RoPE scaling https://github.com/ggerganov/llama.cpp/pull/2268 - YaRN RoPE scaling https://github.com/ggerganov/llama.cpp/pull/2268
- support Baichuan serial models https://github.com/ggerganov/llama.cpp/pull/3009 - support Baichuan serial models https://github.com/ggerganov/llama.cpp/pull/3009
- support attention bias https://github.com/ggerganov/llama.cpp/pull/4283 - support attention bias https://github.com/ggerganov/llama.cpp/pull/4283