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---
datasets:
- Open-Orca/OpenOrca
library_name: transformers
tags:
- llama
---
# Basilisk 4B
[<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)
Built on `winglian/llama-2-4b`, a 4B parameter Llama-2 model, this model is finetuned with open orca CoT data.
```
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
| Task |Version| Metric |Value | |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|agieval_aqua_rat | 0|acc |0.2362|_ |0.0267|
| | |acc_norm |0.2283|_ |0.0264|
|agieval_logiqa_en | 0|acc |0.2688|_ |0.0174|
| | |acc_norm |0.2811|_ |0.0176|
|agieval_lsat_ar | 0|acc |0.2130|_ |0.0271|
| | |acc_norm |0.1913|_ |0.0260|
|agieval_lsat_lr | 0|acc |0.2255|_ |0.0185|
| | |acc_norm |0.2745|_ |0.0198|
|agieval_lsat_rc | 0|acc |0.2305|_ |0.0257|
| | |acc_norm |0.2491|_ |0.0264|
|agieval_sat_en | 0|acc |0.3641|_ |0.0336|
| | |acc_norm |0.3495|_ |0.0333|
|agieval_sat_en_without_passage | 0|acc |0.2427|_ |0.0299|
| | |acc_norm |0.2427|_ |0.0299|
|agieval_sat_math | 0|acc |0.2318|_ |0.0285|
| | |acc_norm |0.2091|_ |0.0275|
|bigbench_causal_judgement | 0|multiple_choice_grade|0.5000|_ |0.0364|
|bigbench_date_understanding | 0|multiple_choice_grade|0.3930|_ |0.0255|
|bigbench_disambiguation_qa | 0|multiple_choice_grade|0.2674|_ |0.0276|
|bigbench_geometric_shapes | 0|multiple_choice_grade|0.1838|_ |0.0205|
| | |exact_str_match |0.0279|_ |0.0087|
|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.2380|_ |0.0191|
|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.1843|_ |0.0147|
|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.3800|_ |0.0281|
|bigbench_movie_recommendation | 0|multiple_choice_grade|0.3480|_ |0.0213|
|bigbench_navigate | 0|multiple_choice_grade|0.5000|_ |0.0158|
|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.3680|_ |0.0108|
|bigbench_ruin_names | 0|multiple_choice_grade|0.2746|_ |0.0211|
|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.2806|_ |0.0142|
|bigbench_snarks | 0|multiple_choice_grade|0.4972|_ |0.0373|
|bigbench_sports_understanding | 0|multiple_choice_grade|0.4939|_ |0.0159|
|bigbench_temporal_sequences | 0|multiple_choice_grade|0.2740|_ |0.0141|
|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.1904|_ |0.0111|
|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1394|_ |0.0083|
|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.3800|_ |0.0281|
hf-causal-experimental (pretrained=winglian/basilisk-4b,use_accelerate=True,trust_remote_code=True), limit: None, provide_description: False, num_fewshot: 0, batch_size: 12
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.3285|_ |0.0137|
| | |acc_norm|0.3532|_ |0.0140|
|arc_easy | 0|acc |0.6364|_ |0.0099|
| | |acc_norm|0.6035|_ |0.0100|
|boolq | 1|acc |0.7196|_ |0.0079|
|hellaswag | 0|acc |0.4239|_ |0.0049|
| | |acc_norm|0.5473|_ |0.0050|
|openbookqa | 0|acc |0.2220|_ |0.0186|
| | |acc_norm|0.3320|_ |0.0211|
|piqa | 0|acc |0.6937|_ |0.0108|
| | |acc_norm|0.6921|_ |0.0108|
|winogrande | 0|acc |0.5399|_ |0.0140|
```
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