README.md

metadata

language:
  - zh
  - en
license: apache-2.0
model-index:
  - name: chinese-alpaca-2-7b
    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: 49.57
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ziqingyang/chinese-alpaca-2-7b
          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: 72.62
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ziqingyang/chinese-alpaca-2-7b
          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: 46.5
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ziqingyang/chinese-alpaca-2-7b
          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: 48.63
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ziqingyang/chinese-alpaca-2-7b
          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: 70.01
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ziqingyang/chinese-alpaca-2-7b
          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: 5.76
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ziqingyang/chinese-alpaca-2-7b
          name: Open LLM Leaderboard

Chinese-Alpaca-2-7B

This is the full Chinese-Alpaca-2-7B model，which can be loaded directly for inference and full-parameter training.

Related models👇

• Long context base models
  • Chinese-LLaMA-2-7B-16K (full model)
  • Chinese-LLaMA-2-LoRA-7B-16K (LoRA model)
  • Chinese-LLaMA-2-13B-16K (full model)
  • Chinese-LLaMA-2-LoRA-13B-16K (LoRA model)
• Base models
  • Chinese-LLaMA-2-7B (full model)
  • Chinese-LLaMA-2-LoRA-7B (LoRA model)
  • Chinese-LLaMA-2-13B (full model)
  • Chinese-LLaMA-2-LoRA-13B (LoRA model)
• Instruction/Chat models
  • Chinese-Alpaca-2-7B (full model)
  • Chinese-Alpaca-2-LoRA-7B (LoRA model)
  • Chinese-Alpaca-2-13B (full model)
  • Chinese-Alpaca-2-LoRA-13B (LoRA model)

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/ for details.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	48.85
AI2 Reasoning Challenge (25-Shot)	49.57
HellaSwag (10-Shot)	72.62
MMLU (5-Shot)	46.50
TruthfulQA (0-shot)	48.63
Winogrande (5-shot)	70.01
GSM8k (5-shot)	5.76