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--- |
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base_model: ethicalabs/xLSTM-7b-Instruct |
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library_name: transformers |
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model_name: xlstm-7b-instruct-phase-2 |
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tags: |
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- sft |
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- transformers |
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- trl |
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licence: license |
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pipeline_tag: text-generation |
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--- |
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# Model Card for xlstm-7b-instruct-phase-2 |
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This model is a fine-tuned version of [ethicalabs/xLSTM-7b-Instruct](https://huggingface.co/ethicalabs/xLSTM-7b-Instruct) for task alignment. |
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It has been trained using [TRL](https://github.com/huggingface/trl) using SFT on assistant-only tokens. |
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The `k_proj` and `v_proj` matrices have been frozen to isolate and preserve the model's pre-trained knowledge base. |
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This fine-tuning focused only on the `q_proj` (query) and FFN matrices, adapting the model's reasoning and query-retrieval mechanisms without overwriting its core, frozen knowledge. |
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This experiment was designed to test the hypothesis that the model's reasoning capabilities (`q_proj`) could be specialized for math/code while its knowledge (`k_proj`, `v_proj`) remained intact. |
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## Quick start |
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Work in Progress! |
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## Training procedure |
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/ethicalabs-ai/xlstm-finetuning-ultrafeedback/runs/zxpd9xeh) |
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This model was trained with SFT. |
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## Evaluation |
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This model has been loaded in 4-bit and evaluated with [lighteval](https://github.com/huggingface/lighteval) |
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| Task |Version| Metric |Value | |Stderr| |
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|------------------------------------------------------|-------|----------------------------------------------------------------------------------------------------------------------------|-----:|---|-----:| |
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|all | |acc |0.5383|± |0.1476| |
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| | |acc:logprob_normalization=LogProbCharNorm(name='norm', ignore_first_space=True) |0.7000|± |0.1528| |
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| | |acc:logprob_normalization=LogProbCharNorm(name='norm', ignore_first_space=False) |0.8000|± |0.1333| |
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| | |truthfulqa_mc1 |0.6000|± |0.1633| |
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| | |truthfulqa_mc2 |0.7066|± |0.1481| |
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| | |em:normalize_gold=<function gsm8k_normalizer at 0x7c5d972c3ba0>&normalize_pred=<function gsm8k_normalizer at 0x7c5d972c3ba0>|0.6000|± |0.1633| |
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|leaderboard:arc:challenge:25 | |acc |0.8000|± |0.1333| |
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| | |acc:logprob_normalization=LogProbCharNorm(name='norm', ignore_first_space=True) |0.7000|± |0.1528| |
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|leaderboard:gsm8k:5 | |em:normalize_gold=<function gsm8k_normalizer at 0x7c5d972c3ba0>&normalize_pred=<function gsm8k_normalizer at 0x7c5d972c3ba0>|0.6000|± |0.1633| |
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|leaderboard:hellaswag:10 | |acc |0.5000|± |0.1667| |
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| | |acc:logprob_normalization=LogProbCharNorm(name='norm', ignore_first_space=False) |0.8000|± |0.1333| |
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|leaderboard:mmlu:_average:5 | |acc |0.5316|± |0.1474| |
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|leaderboard:mmlu:abstract_algebra:5 | |acc |0.3000|± |0.1528| |
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|leaderboard:mmlu:anatomy:5 | |acc |0.3000|± |0.1528| |
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|leaderboard:mmlu:astronomy:5 | |acc |0.7000|± |0.1528| |
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|leaderboard:mmlu:business_ethics:5 | |acc |0.4000|± |0.1633| |
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|leaderboard:mmlu:clinical_knowledge:5 | |acc |0.7000|± |0.1528| |
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|leaderboard:mmlu:college_biology:5 | |acc |0.5000|± |0.1667| |
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|leaderboard:mmlu:college_chemistry:5 | |acc |0.4000|± |0.1633| |
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|leaderboard:mmlu:college_computer_science:5 | |acc |0.4000|± |0.1633| |
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|leaderboard:mmlu:college_mathematics:5 | |acc |0.2000|± |0.1333| |
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|leaderboard:mmlu:college_medicine:5 | |acc |0.5000|± |0.1667| |
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|leaderboard:mmlu:college_physics:5 | |acc |0.5000|± |0.1667| |
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|leaderboard:mmlu:computer_security:5 | |acc |0.9000|± |0.1000| |
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|leaderboard:mmlu:conceptual_physics:5 | |acc |0.4000|± |0.1633| |
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|leaderboard:mmlu:econometrics:5 | |acc |0.4000|± |0.1633| |
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|leaderboard:mmlu:electrical_engineering:5 | |acc |0.7000|± |0.1528| |
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|leaderboard:mmlu:elementary_mathematics:5 | |acc |0.3000|± |0.1528| |
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|leaderboard:mmlu:formal_logic:5 | |acc |0.3000|± |0.1528| |
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|leaderboard:mmlu:global_facts:5 | |acc |0.3000|± |0.1528| |
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|leaderboard:mmlu:high_school_biology:5 | |acc |0.9000|± |0.1000| |
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|leaderboard:mmlu:high_school_chemistry:5 | |acc |0.5000|± |0.1667| |
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|leaderboard:mmlu:high_school_computer_science:5 | |acc |0.6000|± |0.1633| |
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|leaderboard:mmlu:high_school_european_history:5 | |acc |0.7000|± |0.1528| |
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|leaderboard:mmlu:high_school_geography:5 | |acc |1.0000|± |0.0000| |
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|leaderboard:mmlu:high_school_government_and_politics:5| |acc |0.8000|± |0.1333| |
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|leaderboard:mmlu:high_school_macroeconomics:5 | |acc |0.6000|± |0.1633| |
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|leaderboard:mmlu:high_school_mathematics:5 | |acc |0.3000|± |0.1528| |
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|leaderboard:mmlu:high_school_microeconomics:5 | |acc |0.7000|± |0.1528| |
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|leaderboard:mmlu:high_school_physics:5 | |acc |0.3000|± |0.1528| |
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|leaderboard:mmlu:high_school_psychology:5 | |acc |0.9000|± |0.1000| |
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|leaderboard:mmlu:high_school_statistics:5 | |acc |0.5000|± |0.1667| |
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|leaderboard:mmlu:high_school_us_history:5 | |acc |0.8000|± |0.1333| |
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|leaderboard:mmlu:high_school_world_history:5 | |acc |0.9000|± |0.1000| |
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|leaderboard:mmlu:human_aging:5 | |acc |0.5000|± |0.1667| |
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|leaderboard:mmlu:human_sexuality:5 | |acc |0.4000|± |0.1633| |
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|leaderboard:mmlu:international_law:5 | |acc |0.6000|± |0.1633| |
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|leaderboard:mmlu:jurisprudence:5 | |acc |0.6000|± |0.1633| |
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|leaderboard:mmlu:logical_fallacies:5 | |acc |0.4000|± |0.1633| |
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|leaderboard:mmlu:machine_learning:5 | |acc |0.5000|± |0.1667| |
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|leaderboard:mmlu:management:5 | |acc |0.5000|± |0.1667| |
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|leaderboard:mmlu:marketing:5 | |acc |0.8000|± |0.1333| |
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|leaderboard:mmlu:medical_genetics:5 | |acc |0.9000|± |0.1000| |
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|leaderboard:mmlu:miscellaneous:5 | |acc |0.5000|± |0.1667| |
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|leaderboard:mmlu:moral_disputes:5 | |acc |0.7000|± |0.1528| |
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|leaderboard:mmlu:moral_scenarios:5 | |acc |0.1000|± |0.1000| |
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|leaderboard:mmlu:nutrition:5 | |acc |0.6000|± |0.1633| |
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|leaderboard:mmlu:philosophy:5 | |acc |0.5000|± |0.1667| |
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|leaderboard:mmlu:prehistory:5 | |acc |0.4000|± |0.1633| |
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|leaderboard:mmlu:professional_accounting:5 | |acc |0.3000|± |0.1528| |
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|leaderboard:mmlu:professional_law:5 | |acc |0.4000|± |0.1633| |
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|leaderboard:mmlu:professional_medicine:5 | |acc |0.2000|± |0.1333| |
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|leaderboard:mmlu:professional_psychology:5 | |acc |0.3000|± |0.1528| |
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|leaderboard:mmlu:public_relations:5 | |acc |0.3000|± |0.1528| |
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|leaderboard:mmlu:security_studies:5 | |acc |0.3000|± |0.1528| |
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|leaderboard:mmlu:sociology:5 | |acc |0.8000|± |0.1333| |
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|leaderboard:mmlu:us_foreign_policy:5 | |acc |0.7000|± |0.1528| |
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|leaderboard:mmlu:virology:5 | |acc |0.5000|± |0.1667| |
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|leaderboard:mmlu:world_religions:5 | |acc |0.8000|± |0.1333| |
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|leaderboard:truthfulqa:mc:0 | |truthfulqa_mc1 |0.6000|± |0.1633| |
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| | |truthfulqa_mc2 |0.7066|± |0.1481| |
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|leaderboard:winogrande:5 | |acc |0.7000|± |0.1528| |
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### Framework versions |
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- PEFT 0.17.1 |
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- TRL: 0.24.0 |
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- Transformers: 4.57.1 |
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- Pytorch: 2.8.0+cu126 |
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- Datasets: 4.2.0 |
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- Tokenizers: 0.22.1 |
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## Citations |
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Cite TRL as: |
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```bibtex |
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@misc{vonwerra2022trl, |
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title = {{TRL: Transformer Reinforcement Learning}}, |
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author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, |
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year = 2020, |
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journal = {GitHub repository}, |
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publisher = {GitHub}, |
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howpublished = {\url{https://github.com/huggingface/trl}} |
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} |
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``` |
