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--- |
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language: |
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- zh |
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- en |
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license: apache-2.0 |
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model-index: |
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- name: chinese-llama-2-7b |
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results: |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: AI2 Reasoning Challenge (25-Shot) |
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type: ai2_arc |
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config: ARC-Challenge |
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split: test |
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args: |
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num_few_shot: 25 |
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metrics: |
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- type: acc_norm |
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value: 44.45 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ziqingyang/chinese-llama-2-7b |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: HellaSwag (10-Shot) |
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type: hellaswag |
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split: validation |
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args: |
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num_few_shot: 10 |
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metrics: |
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- type: acc_norm |
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value: 69.5 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ziqingyang/chinese-llama-2-7b |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MMLU (5-Shot) |
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type: cais/mmlu |
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config: all |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 37.47 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ziqingyang/chinese-llama-2-7b |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: TruthfulQA (0-shot) |
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type: truthful_qa |
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config: multiple_choice |
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split: validation |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: mc2 |
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value: 37.0 |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ziqingyang/chinese-llama-2-7b |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: Winogrande (5-shot) |
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type: winogrande |
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config: winogrande_xl |
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split: validation |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 68.98 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ziqingyang/chinese-llama-2-7b |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: GSM8k (5-shot) |
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type: gsm8k |
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config: main |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 1.44 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ziqingyang/chinese-llama-2-7b |
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name: Open LLM Leaderboard |
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--- |
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# Chinese-LLaMA-2-7B |
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**This is the full Chinese-LLaMA-2-7B model,which can be loaded directly for inference and full-parameter training.** |
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**Related models👇** |
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* Long context base models |
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* [Chinese-LLaMA-2-7B-16K (full model)](https://huggingface.co/hfl/chinese-llama-2-7b-16k) |
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* [Chinese-LLaMA-2-LoRA-7B-16K (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-7b-16k) |
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* [Chinese-LLaMA-2-13B-16K (full model)](https://huggingface.co/hfl/chinese-llama-2-13b-16k) |
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* [Chinese-LLaMA-2-LoRA-13B-16K (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-13b-16k) |
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* Base models |
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* [Chinese-LLaMA-2-7B (full model)](https://huggingface.co/hfl/chinese-llama-2-7b) |
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* [Chinese-LLaMA-2-LoRA-7B (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-7b) |
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* [Chinese-LLaMA-2-13B (full model)](https://huggingface.co/hfl/chinese-llama-2-13b) |
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* [Chinese-LLaMA-2-LoRA-13B (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-13b) |
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* Instruction/Chat models |
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* [Chinese-Alpaca-2-7B (full model)](https://huggingface.co/hfl/chinese-alpaca-2-7b) |
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* [Chinese-Alpaca-2-LoRA-7B (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-7b) |
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* [Chinese-Alpaca-2-13B (full model)](https://huggingface.co/hfl/chinese-alpaca-2-13b) |
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* [Chinese-Alpaca-2-LoRA-13B (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-13b) |
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# Description of Chinese-LLaMA-Alpaca-2 |
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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. |
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The main contents of this project include: |
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* 🚀 New extended Chinese vocabulary beyond Llama-2, open-sourcing the Chinese LLaMA-2 and Alpaca-2 LLMs. |
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* 🚀 Open-sourced the pre-training and instruction finetuning (SFT) scripts for further tuning on user's data |
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* 🚀 Quickly deploy and experience the quantized LLMs on CPU/GPU of personal PC |
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* 🚀 Support for LLaMA ecosystems like 🤗transformers, llama.cpp, text-generation-webui, LangChain, vLLM etc. |
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Please refer to [https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/) for details. |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ziqingyang__chinese-llama-2-7b) |
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| Metric |Value| |
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|---------------------------------|----:| |
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|Avg. |43.14| |
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|AI2 Reasoning Challenge (25-Shot)|44.45| |
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|HellaSwag (10-Shot) |69.50| |
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|MMLU (5-Shot) |37.47| |
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|TruthfulQA (0-shot) |37.00| |
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|Winogrande (5-shot) |68.98| |
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|GSM8k (5-shot) | 1.44| |
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