---
base_model: ethicalabs/xLSTM-7b-Instruct
library_name: transformers
model_name: xlstm-7b-instruct-phase-2
tags:
- sft
- transformers
- trl
licence: license
pipeline_tag: text-generation
---
# Model Card for xlstm-7b-instruct-phase-2
This model is a fine-tuned version of [ethicalabs/xLSTM-7b-Instruct](https://huggingface.co/ethicalabs/xLSTM-7b-Instruct) for task alignment.
It has been trained using [TRL](https://github.com/huggingface/trl) using SFT on assistant-only tokens.
The `k_proj` and `v_proj` matrices have been frozen to isolate and preserve the model's pre-trained knowledge base.
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.
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.
## Quick start
Work in Progress!
## Training procedure
[
](https://wandb.ai/ethicalabs-ai/xlstm-finetuning-ultrafeedback/runs/zxpd9xeh)
This model was trained with SFT.
## Evaluation
This model has been loaded in 4-bit and evaluated with [lighteval](https://github.com/huggingface/lighteval)
| Task |Version| Metric |Value | |Stderr|
|------------------------------------------------------|-------|----------------------------------------------------------------------------------------------------------------------------|-----:|---|-----:|
|all | |acc |0.5383|± |0.1476|
| | |acc:logprob_normalization=LogProbCharNorm(name='norm', ignore_first_space=True) |0.7000|± |0.1528|
| | |acc:logprob_normalization=LogProbCharNorm(name='norm', ignore_first_space=False) |0.8000|± |0.1333|
| | |truthfulqa_mc1 |0.6000|± |0.1633|
| | |truthfulqa_mc2 |0.7066|± |0.1481|
| | |em:normalize_gold=&normalize_pred=|0.6000|± |0.1633|
|leaderboard:arc:challenge:25 | |acc |0.8000|± |0.1333|
| | |acc:logprob_normalization=LogProbCharNorm(name='norm', ignore_first_space=True) |0.7000|± |0.1528|
|leaderboard:gsm8k:5 | |em:normalize_gold=&normalize_pred=|0.6000|± |0.1633|
|leaderboard:hellaswag:10 | |acc |0.5000|± |0.1667|
| | |acc:logprob_normalization=LogProbCharNorm(name='norm', ignore_first_space=False) |0.8000|± |0.1333|
|leaderboard:mmlu:_average:5 | |acc |0.5316|± |0.1474|
|leaderboard:mmlu:abstract_algebra:5 | |acc |0.3000|± |0.1528|
|leaderboard:mmlu:anatomy:5 | |acc |0.3000|± |0.1528|
|leaderboard:mmlu:astronomy:5 | |acc |0.7000|± |0.1528|
|leaderboard:mmlu:business_ethics:5 | |acc |0.4000|± |0.1633|
|leaderboard:mmlu:clinical_knowledge:5 | |acc |0.7000|± |0.1528|
|leaderboard:mmlu:college_biology:5 | |acc |0.5000|± |0.1667|
|leaderboard:mmlu:college_chemistry:5 | |acc |0.4000|± |0.1633|
|leaderboard:mmlu:college_computer_science:5 | |acc |0.4000|± |0.1633|
|leaderboard:mmlu:college_mathematics:5 | |acc |0.2000|± |0.1333|
|leaderboard:mmlu:college_medicine:5 | |acc |0.5000|± |0.1667|
|leaderboard:mmlu:college_physics:5 | |acc |0.5000|± |0.1667|
|leaderboard:mmlu:computer_security:5 | |acc |0.9000|± |0.1000|
|leaderboard:mmlu:conceptual_physics:5 | |acc |0.4000|± |0.1633|
|leaderboard:mmlu:econometrics:5 | |acc |0.4000|± |0.1633|
|leaderboard:mmlu:electrical_engineering:5 | |acc |0.7000|± |0.1528|
|leaderboard:mmlu:elementary_mathematics:5 | |acc |0.3000|± |0.1528|
|leaderboard:mmlu:formal_logic:5 | |acc |0.3000|± |0.1528|
|leaderboard:mmlu:global_facts:5 | |acc |0.3000|± |0.1528|
|leaderboard:mmlu:high_school_biology:5 | |acc |0.9000|± |0.1000|
|leaderboard:mmlu:high_school_chemistry:5 | |acc |0.5000|± |0.1667|
|leaderboard:mmlu:high_school_computer_science:5 | |acc |0.6000|± |0.1633|
|leaderboard:mmlu:high_school_european_history:5 | |acc |0.7000|± |0.1528|
|leaderboard:mmlu:high_school_geography:5 | |acc |1.0000|± |0.0000|
|leaderboard:mmlu:high_school_government_and_politics:5| |acc |0.8000|± |0.1333|
|leaderboard:mmlu:high_school_macroeconomics:5 | |acc |0.6000|± |0.1633|
|leaderboard:mmlu:high_school_mathematics:5 | |acc |0.3000|± |0.1528|
|leaderboard:mmlu:high_school_microeconomics:5 | |acc |0.7000|± |0.1528|
|leaderboard:mmlu:high_school_physics:5 | |acc |0.3000|± |0.1528|
|leaderboard:mmlu:high_school_psychology:5 | |acc |0.9000|± |0.1000|
|leaderboard:mmlu:high_school_statistics:5 | |acc |0.5000|± |0.1667|
|leaderboard:mmlu:high_school_us_history:5 | |acc |0.8000|± |0.1333|
|leaderboard:mmlu:high_school_world_history:5 | |acc |0.9000|± |0.1000|
|leaderboard:mmlu:human_aging:5 | |acc |0.5000|± |0.1667|
|leaderboard:mmlu:human_sexuality:5 | |acc |0.4000|± |0.1633|
|leaderboard:mmlu:international_law:5 | |acc |0.6000|± |0.1633|
|leaderboard:mmlu:jurisprudence:5 | |acc |0.6000|± |0.1633|
|leaderboard:mmlu:logical_fallacies:5 | |acc |0.4000|± |0.1633|
|leaderboard:mmlu:machine_learning:5 | |acc |0.5000|± |0.1667|
|leaderboard:mmlu:management:5 | |acc |0.5000|± |0.1667|
|leaderboard:mmlu:marketing:5 | |acc |0.8000|± |0.1333|
|leaderboard:mmlu:medical_genetics:5 | |acc |0.9000|± |0.1000|
|leaderboard:mmlu:miscellaneous:5 | |acc |0.5000|± |0.1667|
|leaderboard:mmlu:moral_disputes:5 | |acc |0.7000|± |0.1528|
|leaderboard:mmlu:moral_scenarios:5 | |acc |0.1000|± |0.1000|
|leaderboard:mmlu:nutrition:5 | |acc |0.6000|± |0.1633|
|leaderboard:mmlu:philosophy:5 | |acc |0.5000|± |0.1667|
|leaderboard:mmlu:prehistory:5 | |acc |0.4000|± |0.1633|
|leaderboard:mmlu:professional_accounting:5 | |acc |0.3000|± |0.1528|
|leaderboard:mmlu:professional_law:5 | |acc |0.4000|± |0.1633|
|leaderboard:mmlu:professional_medicine:5 | |acc |0.2000|± |0.1333|
|leaderboard:mmlu:professional_psychology:5 | |acc |0.3000|± |0.1528|
|leaderboard:mmlu:public_relations:5 | |acc |0.3000|± |0.1528|
|leaderboard:mmlu:security_studies:5 | |acc |0.3000|± |0.1528|
|leaderboard:mmlu:sociology:5 | |acc |0.8000|± |0.1333|
|leaderboard:mmlu:us_foreign_policy:5 | |acc |0.7000|± |0.1528|
|leaderboard:mmlu:virology:5 | |acc |0.5000|± |0.1667|
|leaderboard:mmlu:world_religions:5 | |acc |0.8000|± |0.1333|
|leaderboard:truthfulqa:mc:0 | |truthfulqa_mc1 |0.6000|± |0.1633|
| | |truthfulqa_mc2 |0.7066|± |0.1481|
|leaderboard:winogrande:5 | |acc |0.7000|± |0.1528|
### Framework versions
- PEFT 0.17.1
- TRL: 0.24.0
- Transformers: 4.57.1
- Pytorch: 2.8.0+cu126
- Datasets: 4.2.0
- Tokenizers: 0.22.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
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},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```