--- language: - zh - en license: apache-2.0 model-index: - name: chinese-alpaca-2-13b-16k results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 55.03 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=hfl/chinese-alpaca-2-13b-16k name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 77.41 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=hfl/chinese-alpaca-2-13b-16k name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 51.28 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=hfl/chinese-alpaca-2-13b-16k name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 46.5 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=hfl/chinese-alpaca-2-13b-16k name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 73.4 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=hfl/chinese-alpaca-2-13b-16k name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 21.08 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=hfl/chinese-alpaca-2-13b-16k name: Open LLM Leaderboard --- # Chinese-LLaMA-2-13B-16K **This is the full Chinese-LLaMA-2-13B-16K (context size 16K)，model，which can be loaded directly for inference and full-parameter training.** **Related models👇** * Long context base models (16K) * [Chinese-LLaMA-2-7B-16K (full model)](https://huggingface.co/hfl/chinese-llama-2-7b-16k) * [Chinese-LLaMA-2-LoRA-7B-16K (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-7b-16k) * [Chinese-LLaMA-2-13B-16K (full model)](https://huggingface.co/hfl/chinese-llama-2-13b-16k) * [Chinese-LLaMA-2-LoRA-13B-16K (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-13b-16k) * Long context Instruction/Chat models * [Chinese-Alpaca-2-7B-16K (full model)](https://huggingface.co/hfl/chinese-alpaca-2-7b-16k) * [Chinese-Alpaca-2-LoRA-7B-16K (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-7b-16k) * [Chinese-Alpaca-2-13B-16K (full model)](https://huggingface.co/hfl/chinese-alpaca-2-13b-16k) * [Chinese-Alpaca-2-LoRA-13B-16K (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-13b-16k) * Base models * [Chinese-LLaMA-2-7B (full model)](https://huggingface.co/hfl/chinese-llama-2-7b) * [Chinese-LLaMA-2-LoRA-7B (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-7b) * [Chinese-LLaMA-2-13B (full model)](https://huggingface.co/hfl/chinese-llama-2-13b) * [Chinese-LLaMA-2-LoRA-13B (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-13b) * Instruction/Chat models * [Chinese-Alpaca-2-7B (full model)](https://huggingface.co/hfl/chinese-alpaca-2-7b) * [Chinese-Alpaca-2-LoRA-7B (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-7b) * [Chinese-Alpaca-2-13B (full model)](https://huggingface.co/hfl/chinese-alpaca-2-13b) * [Chinese-Alpaca-2-LoRA-13B (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-13b) # Description of Chinese-LLaMA-Alpaca-2 This project is based on the Llama-2, released by Meta, and it is the second generation of the Chinese LLaMA & Alpaca LLM project. We open-source Chinese LLaMA-2 (foundation model) and Alpaca-2 (instruction-following model). These models have been expanded and optimized with Chinese vocabulary beyond the original Llama-2. We used large-scale Chinese data for incremental pre-training, which further improved the fundamental semantic understanding of the Chinese language, resulting in a significant performance improvement compared to the first-generation models. The relevant models support a 4K context and can be expanded up to 18K+ using the NTK method. The main contents of this project include: * 🚀 New extended Chinese vocabulary beyond Llama-2, open-sourcing the Chinese LLaMA-2 and Alpaca-2 LLMs. * 🚀 Open-sourced the pre-training and instruction finetuning (SFT) scripts for further tuning on user's data * 🚀 Quickly deploy and experience the quantized LLMs on CPU/GPU of personal PC * 🚀 Support for LLaMA ecosystems like 🤗transformers, llama.cpp, text-generation-webui, LangChain, vLLM etc. Please refer to [https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/) for details. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_hfl__chinese-alpaca-2-13b-16k) | Metric |Value| |---------------------------------|----:| |Avg. |54.12| |AI2 Reasoning Challenge (25-Shot)|55.03| |HellaSwag (10-Shot) |77.41| |MMLU (5-Shot) |51.28| |TruthfulQA (0-shot) |46.50| |Winogrande (5-shot) |73.40| |GSM8k (5-shot) |21.08|