Taiwan-LLM-13B-v2.0-chat-GGUF - GGUF
Description
This repo contains GGUF format model files for Yen-Ting Lin's
Taiwan LLM based on LLaMa2-13b.
Any utilization of the Taiwan LLM repository mandates the explicit acknowledgment and attribution to the original author.
使用Taiwan LLM必須明確地承認和歸功於優必達株式會社 Ubitus 以及原始作者。
About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
The key benefit of GGUF is that it is a extensible, future-proof format which stores more information about the model as metadata. It also includes significantly improved tokenization code, including for the first time full support for special tokens. This should improve performance, especially with models that use new special tokens and implement custom prompt templates.
As of August 25th, here is a list of clients and libraries that are known to support GGUF:
- llama.cpp.
- text-generation-webui, the most widely used web UI. Supports GGUF with GPU acceleration via the ctransformers backend - llama-cpp-python backend should work soon too.
- KoboldCpp, now supports GGUF as of release 1.41! A powerful GGML web UI, with full GPU accel. Especially good for story telling.
- LM Studio, version 0.2.2 and later support GGUF. A fully featured local GUI with GPU acceleration on both Windows (NVidia and AMD), and macOS.
- LoLLMS Web UI, should now work, choose the
c_transformers backend. A great web UI with many interesting features. Supports CUDA GPU acceleration.
- ctransformers, now supports GGUF as of version 0.2.24! A Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
- llama-cpp-python, supports GGUF as of version 0.1.79. A Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
- candle, added GGUF support on August 22nd. Candle is a Rust ML framework with a focus on performance, including GPU support, and ease of use.
Original model card
Taiwan LLM based on LLaMa2-13b
continue pretraining on 20 billion tokens in traditional Mandarin and instruction fine-tuning on millions of conversations.
This version does NOT include CommonCrawl.
Collaboration with Ubitus K.K. 💪💪💪
本項目與 Ubitus K.K. 合作進行。Ubitus 為本項目提供寶貴的技術支持和計算資源。
Taiwan LLM v2 is conducted in collaboration with
Ubitus K.K.. Ubitus provides valuable technical support and compute resources for the project.
