--- base_model: ethicalabs/xLSTM-7b-Instruct library_name: peft model_name: xlstm-7b-instruct-phase-2 tags: - lora - sft - transformers - trl licence: license pipeline_tag: text-generation datasets: - teknium/OpenHermes-2.5 - meta-math/MetaMathQA - trl-lib/ultrafeedback-gpt-3.5-turbo-helpfulness license: mit language: - en --- # 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 [Visualize in Weights & Biases](https://wandb.ai/ethicalabs-ai/xlstm-finetuning-ultrafeedback/runs/zxpd9xeh) This model was trained with SFT. ### 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}} } ```