Llm-rs

Added the ability to automatically convert any supported model from the Huggingface Hub via the `AutoConverter`.


Models which were converted this way, can be easily quantized or loaded via the `AutoQuantizer` or `AutoModel` without the need to specifiy the architecture.

The ability to quantize models is now available for every architecture via `quantize`.

- Added support for [Mosaic ML](https://huggingface.co/mosaicml)'s MPT models.
- Added support for [LoRA](https://arxiv.org/abs/2106.09685) adapters for all architectures.

⚠️Caution⚠️
Due to changes in the ggml format old quantized models are not supported anymore!

Added the `tokenize` and `decode` functions to each model, to enable access to the internal tokenizer.

The generation of tokens is now GIL free, meaning other background threads can run at the same time.

Since `llama-rs` was renamed to `llm` and now supports multiple model architectures, this wrapper was also expanded to support the new trait system and library structure.

Supported architectures for now:
- Llama
- GPT2
- GPTJ
- GPT-NeoX
- Bloom

The loader was also reworked and now supports the mmap-able `ggjt`. To support this the `SessionConfig` was expandend with the `prefer_mmap` field.