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README.md
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# Model Card for Kurtis-E1.1-Qwen2.5-3B-Instruct
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Kurtis E1.1 fine-tuned with [flower](https://flower.ai/)
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# Model Card for Kurtis-E1.1-Qwen2.5-3B-Instruct
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Kurtis E1.1 fine-tuned with [flower](https://flower.ai/)
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## Eval Results
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Evaluation tasks were performed with the [LM Evaluation Harness] (https://github.com/EleutherAI/lm-evaluation-harness) on an NVIDIA A40.
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### hellaswag
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```
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lm_eval --model hf --model_args pretrained=ethicalabs/Kurtis-E1.1-Qwen2.5-3B-Instruct --tasks hellaswag --device cuda:0 --batch_size 8
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```
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| Tasks |Version|Filter|n-shot| Metric | |Value | |Stderr|
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|---------|------:|------|-----:|--------|---|-----:|---|-----:|
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|hellaswag| 1|none | 0|acc |↑ |0.5555|± |0.0050|
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| | |none | 0|acc_norm|↑ |0.7412|± |0.0044|
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### arc_easy
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```
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lm_eval --model hf --model_args pretrained=ethicalabs/Kurtis-E1.1-Qwen2.5-3B-Instruct --tasks arc_easy --device cuda:0 --batch_size 8
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```
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| Tasks |Version|Filter|n-shot| Metric | |Value | |Stderr|
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|--------|------:|------|-----:|--------|---|-----:|---|-----:|
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|arc_easy| 1|none | 0|acc |↑ |0.7710|± |0.0086|
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| | |none | 0|acc_norm|↑ |0.6789|± |0.0096|
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### arc_challenge
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```
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lm_eval --model hf --model_args pretrained=ethicalabs/Kurtis-E1.1-Qwen2.5-3B-Instruct --tasks arc_challenge --device cuda:0 --batch_size 8
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```
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| Tasks |Version|Filter|n-shot| Metric | |Value| |Stderr|
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|-------------|------:|------|-----:|--------|---|----:|---|-----:|
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|arc_challenge| 1|none | 0|acc |↑ |0.436|± |0.0145|
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| | |none | 0|acc_norm|↑ |0.448|± |0.0145|
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### mmlu
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```
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lm_eval --model hf --model_args pretrained=ethicalabs/Kurtis-E1.1-Qwen2.5-3B-Instruct --tasks mmlu --device cuda:0 --batch_size 8
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```
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| Tasks |Version|Filter|n-shot|Metric| |Value | |Stderr|
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|---------------------------------------|------:|------|-----:|------|---|-----:|---|-----:|
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|mmlu | 2|none | |acc |↑ |0.6522|± |0.0038|
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| - humanities | 2|none | |acc |↑ |0.5734|± |0.0066|
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| - formal_logic | 1|none | 0|acc |↑ |0.4603|± |0.0446|
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| - high_school_european_history | 1|none | 0|acc |↑ |0.7939|± |0.0316|
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| - high_school_us_history | 1|none | 0|acc |↑ |0.8333|± |0.0262|
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| - high_school_world_history | 1|none | 0|acc |↑ |0.8397|± |0.0239|
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| - international_law | 1|none | 0|acc |↑ |0.7769|± |0.0380|
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| - jurisprudence | 1|none | 0|acc |↑ |0.7963|± |0.0389|
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| - logical_fallacies | 1|none | 0|acc |↑ |0.7975|± |0.0316|
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| - moral_disputes | 1|none | 0|acc |↑ |0.6850|± |0.0250|
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| - moral_scenarios | 1|none | 0|acc |↑ |0.2905|± |0.0152|
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| - philosophy | 1|none | 0|acc |↑ |0.7106|± |0.0258|
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| - prehistory | 1|none | 0|acc |↑ |0.7438|± |0.0243|
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| - professional_law | 1|none | 0|acc |↑ |0.4759|± |0.0128|
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| - world_religions | 1|none | 0|acc |↑ |0.8246|± |0.0292|
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| - other | 2|none | |acc |↑ |0.7087|± |0.0079|
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| - business_ethics | 1|none | 0|acc |↑ |0.7300|± |0.0446|
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| - clinical_knowledge | 1|none | 0|acc |↑ |0.7321|± |0.0273|
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| - college_medicine | 1|none | 0|acc |↑ |0.6705|± |0.0358|
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| - global_facts | 1|none | 0|acc |↑ |0.3900|± |0.0490|
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| - human_aging | 1|none | 0|acc |↑ |0.7130|± |0.0304|
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| - management | 1|none | 0|acc |↑ |0.7961|± |0.0399|
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| - marketing | 1|none | 0|acc |↑ |0.8803|± |0.0213|
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| - medical_genetics | 1|none | 0|acc |↑ |0.7600|± |0.0429|
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| - miscellaneous | 1|none | 0|acc |↑ |0.7957|± |0.0144|
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| - nutrition | 1|none | 0|acc |↑ |0.7353|± |0.0253|
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| - professional_accounting | 1|none | 0|acc |↑ |0.5426|± |0.0297|
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| - professional_medicine | 1|none | 0|acc |↑ |0.6434|± |0.0291|
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| - virology | 1|none | 0|acc |↑ |0.4880|± |0.0389|
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| - social sciences | 2|none | |acc |↑ |0.7618|± |0.0076|
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| - econometrics | 1|none | 0|acc |↑ |0.5439|± |0.0469|
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| - high_school_geography | 1|none | 0|acc |↑ |0.7677|± |0.0301|
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| - high_school_government_and_politics| 1|none | 0|acc |↑ |0.8860|± |0.0229|
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| - high_school_macroeconomics | 1|none | 0|acc |↑ |0.6949|± |0.0233|
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| - high_school_microeconomics | 1|none | 0|acc |↑ |0.7773|± |0.0270|
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| - high_school_psychology | 1|none | 0|acc |↑ |0.8477|± |0.0154|
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| - human_sexuality | 1|none | 0|acc |↑ |0.7786|± |0.0364|
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| - professional_psychology | 1|none | 0|acc |↑ |0.7075|± |0.0184|
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| - public_relations | 1|none | 0|acc |↑ |0.6818|± |0.0446|
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| - security_studies | 1|none | 0|acc |↑ |0.7224|± |0.0287|
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| - sociology | 1|none | 0|acc |↑ |0.8458|± |0.0255|
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| - us_foreign_policy | 1|none | 0|acc |↑ |0.8400|± |0.0368|
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| - stem | 2|none | |acc |↑ |0.6070|± |0.0085|
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| - abstract_algebra | 1|none | 0|acc |↑ |0.4700|± |0.0502|
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| - anatomy | 1|none | 0|acc |↑ |0.6667|± |0.0407|
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| - astronomy | 1|none | 0|acc |↑ |0.6776|± |0.0380|
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| - college_biology | 1|none | 0|acc |↑ |0.7222|± |0.0375|
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| - college_chemistry | 1|none | 0|acc |↑ |0.5000|± |0.0503|
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| - college_computer_science | 1|none | 0|acc |↑ |0.6000|± |0.0492|
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| - college_mathematics | 1|none | 0|acc |↑ |0.3400|± |0.0476|
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| - college_physics | 1|none | 0|acc |↑ |0.4902|± |0.0497|
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| - computer_security | 1|none | 0|acc |↑ |0.7000|± |0.0461|
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| - conceptual_physics | 1|none | 0|acc |↑ |0.6468|± |0.0312|
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| - electrical_engineering | 1|none | 0|acc |↑ |0.6690|± |0.0392|
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| - elementary_mathematics | 1|none | 0|acc |↑ |0.5979|± |0.0253|
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| - high_school_biology | 1|none | 0|acc |↑ |0.8129|± |0.0222|
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| - high_school_chemistry | 1|none | 0|acc |↑ |0.5813|± |0.0347|
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| - high_school_computer_science | 1|none | 0|acc |↑ |0.7800|± |0.0416|
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| - high_school_mathematics | 1|none | 0|acc |↑ |0.5037|± |0.0305|
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| - high_school_physics | 1|none | 0|acc |↑ |0.4437|± |0.0406|
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| - high_school_statistics | 1|none | 0|acc |↑ |0.5972|± |0.0334|
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| - machine_learning | 1|none | 0|acc |↑ |0.4554|± |0.0473|
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| Groups |Version|Filter|n-shot|Metric| |Value | |Stderr|
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|------------------|------:|------|------|------|---|-----:|---|-----:|
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|mmlu | 2|none | |acc |↑ |0.6522|± |0.0038|
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| - humanities | 2|none | |acc |↑ |0.5734|± |0.0066|
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| - other | 2|none | |acc |↑ |0.7087|± |0.0079|
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| - social sciences| 2|none | |acc |↑ |0.7618|± |0.0076|
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| - stem | 2|none | |acc |↑ |0.6070|± |0.0085|
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