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--- |
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language: |
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- en |
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license: llama2 |
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tags: |
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- information retrieval |
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- reranker |
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inference: false |
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model-index: |
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- name: rank_vicuna_7b_v1_fp16 |
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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: 44.62 |
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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=castorini/rank_vicuna_7b_v1_fp16 |
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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: 65.67 |
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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=castorini/rank_vicuna_7b_v1_fp16 |
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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: 44.14 |
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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=castorini/rank_vicuna_7b_v1_fp16 |
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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: 45.13 |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=castorini/rank_vicuna_7b_v1_fp16 |
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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: 66.61 |
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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=castorini/rank_vicuna_7b_v1_fp16 |
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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: 0.0 |
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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=castorini/rank_vicuna_7b_v1_fp16 |
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name: Open LLM Leaderboard |
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--- |
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# RankVicuna (FP16) Model Card |
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## Model Details |
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RankVicuna is a chat assistant trained by fine-tuning Llama 2 on user-shared conversations collected from ShareGPT. |
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- **Developed by:** [Castorini](https://github.com/castorini) |
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- **Model type:** An auto-regressive language model based on the transformer architecture |
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- **License:** Llama 2 Community License Agreement |
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- **Finetuned from base model:** [Llama 2](https://arxiv.org/abs/2307.09288) |
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This specific model is a 7B variant and is trained with data augmentation. |
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It is also worth noting that it is converted to FP16. |
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### Model Sources |
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- **Repository:** https://github.com/castorini/rank_llm |
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- **Paper:** https://arxiv.org/abs/2309.15088 |
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## Uses |
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The primary use of RankVicuna is research at the intersection of large language models and retrieval. |
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The primary intended users of the model are researchers and hobbyists in natural language processing and information retrieval. |
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## Training Details |
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RankVicuna is finetuned from `lmsys/vicuna-7b-v1.5` with supervised instruction fine-tuning. |
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## Evaluation |
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RankVicuna is currently evaluated on DL19/DL20. See more details in our [paper](https://arxiv.org/pdf/2309.15088.pdf). |
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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_castorini__rank_vicuna_7b_v1_fp16) |
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| Metric |Value| |
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|---------------------------------|----:| |
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|Avg. |44.36| |
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|AI2 Reasoning Challenge (25-Shot)|44.62| |
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|HellaSwag (10-Shot) |65.67| |
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|MMLU (5-Shot) |44.14| |
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|TruthfulQA (0-shot) |45.13| |
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|Winogrande (5-shot) |66.61| |
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|GSM8k (5-shot) | 0.00| |
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