Create README.md
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README.md
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---
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datasets:
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- Open-Orca/OpenOrca
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library_name: transformers
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tags:
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- llama
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---
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# Basilisk 4B
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
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Built on `winglian/llama-2-4b`, a 4B parameter Llama-2 model, this model is finetuned with open orca CoT data.
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```
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hf-causal-experimental (pretrained=winglian/basilisk-4b,use_accelerate=True,trust_remote_code=True), limit: None, provide_description: False, num_fewshot: 0, batch_size: None
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| Task |Version| Metric |Value | |Stderr|
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|------------------------------------------------|------:|---------------------|-----:|---|-----:|
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|agieval_aqua_rat | 0|acc |0.2362|_ |0.0267|
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| | |acc_norm |0.2283|_ |0.0264|
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|agieval_logiqa_en | 0|acc |0.2688|_ |0.0174|
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| | |acc_norm |0.2811|_ |0.0176|
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|agieval_lsat_ar | 0|acc |0.2130|_ |0.0271|
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| | |acc_norm |0.1913|_ |0.0260|
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|agieval_lsat_lr | 0|acc |0.2255|_ |0.0185|
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| | |acc_norm |0.2745|_ |0.0198|
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|agieval_lsat_rc | 0|acc |0.2305|_ |0.0257|
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| | |acc_norm |0.2491|_ |0.0264|
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|agieval_sat_en | 0|acc |0.3641|_ |0.0336|
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| | |acc_norm |0.3495|_ |0.0333|
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|agieval_sat_en_without_passage | 0|acc |0.2427|_ |0.0299|
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| | |acc_norm |0.2427|_ |0.0299|
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|agieval_sat_math | 0|acc |0.2318|_ |0.0285|
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| | |acc_norm |0.2091|_ |0.0275|
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|bigbench_causal_judgement | 0|multiple_choice_grade|0.5000|_ |0.0364|
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|bigbench_date_understanding | 0|multiple_choice_grade|0.3930|_ |0.0255|
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|bigbench_disambiguation_qa | 0|multiple_choice_grade|0.2674|_ |0.0276|
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|bigbench_geometric_shapes | 0|multiple_choice_grade|0.1838|_ |0.0205|
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| | |exact_str_match |0.0279|_ |0.0087|
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|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.2380|_ |0.0191|
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|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.1843|_ |0.0147|
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|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.3800|_ |0.0281|
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|bigbench_movie_recommendation | 0|multiple_choice_grade|0.3480|_ |0.0213|
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|bigbench_navigate | 0|multiple_choice_grade|0.5000|_ |0.0158|
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|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.3680|_ |0.0108|
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|bigbench_ruin_names | 0|multiple_choice_grade|0.2746|_ |0.0211|
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|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.2806|_ |0.0142|
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|bigbench_snarks | 0|multiple_choice_grade|0.4972|_ |0.0373|
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|bigbench_sports_understanding | 0|multiple_choice_grade|0.4939|_ |0.0159|
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|bigbench_temporal_sequences | 0|multiple_choice_grade|0.2740|_ |0.0141|
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|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.1904|_ |0.0111|
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|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1394|_ |0.0083|
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|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.3800|_ |0.0281|
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```
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