**Hot topics:**
- [Roadmap (short-term)](https://github.com/ggerganov/llama.cpp/discussions/457)
-- New C-style API is now available: https://github.com/ggerganov/llama.cpp/pull/370
-- Cache input prompts for faster initialization: https://github.com/ggerganov/llama.cpp/issues/64
-- Create a `llama.cpp` logo: https://github.com/ggerganov/llama.cpp/issues/105
+- Support for [GPT4All](https://github.com/ggerganov/llama.cpp#using-gpt4all)
## Description
- [X] Windows (via CMake)
- [X] Docker
+Supported models:
+
+- [X] LLaMA
+- [X] [Alpaca](https://github.com/ggerganov/llama.cpp#instruction-mode-with-alpaca)
+- [X] [GPT4All](https://github.com/ggerganov/llama.cpp#using-gpt4all)
+
---
Here is a typical run using LLaMA-7B:
>
```
+### Using [GPT4All](https://github.com/nomic-ai/gpt4all)
+
+- Obtain the `gpt4all-lora-quantized.bin` model
+- It is distributed in the old `ggml` format which is not obsoleted. So you have to convert it to the new format using [./convert-gpt4all-to-ggml.py](./convert-gpt4all-to-ggml.py):
+
+ ```bash
+ python3 convert-gpt4all-to-ggml.py models/gpt4all-7B/gpt4all-lora-quantized.bin ./models/tokenizer.model
+ ```
+
+- You can now use the newly generated `gpt4all-lora-quantized.bin` model in exactly the same way as all other models. The original model is stored in the same folder with a suffix `.orig`
+
### Obtaining and verifying the Facebook LLaMA original model and Stanford Alpaca model data
- **Under no circumstances share IPFS, magnet links, or any other links to model downloads anywhere in this respository, including in issues, discussions or pull requests. They will be immediately deleted.**