LFM2.5 350M Tool Calling LoRA v2

--- base_model: mosama/LFM2.5-350M-Tool-Calling-Merged-v2 library_name: transformers model_name: LFM2.5-350M-Tool-Calling-LORA-v2 tags: - generated_from_trainer - trl - unsloth - sft licence: license --- # Model Card for LFM2.5-350M-Tool-Calling-LORA-v2 This model is a fine-tuned version of [mosama/LFM2.5-350M-Tool-Calling-Merged-v2](https://huggingface.co/mosama/LFM2.5-350M-Tool-Calling-Merged-v2). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="mosama/LFM2.5-350M-Tool-Calling-LORA-v2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<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/muhammadosama1994/LFM%202.5%20Training/runs/heylb619) This model was trained with SFT. ### Framework versions - TRL: 0.22.2 - Transformers: 4.56.2 - Pytorch: 2.10.0+cu128 - Datasets: 4.3.0 - Tokenizers: 0.22.2 ## 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}} } ```