Instructions to use DesaiJayM/Bharat-24B-Math-Code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use DesaiJayM/Bharat-24B-Math-Code with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-Small-3.2-24B-Instruct-2506") model = PeftModel.from_pretrained(base_model, "DesaiJayM/Bharat-24B-Math-Code") - Notebooks
- Google Colab
- Kaggle
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Download DATA_ATTRIBUTION.md from DesaiJayM/Bharat-24B-Math-Code: direct link, hf CLI and curl.
- Browser
- Download file 1.09 kB
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https://huggingface.co/DesaiJayM/Bharat-24B-Math-Code/resolve/main/DATA_ATTRIBUTION.md
- Command line
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hf download hf://DesaiJayM/Bharat-24B-Math-Code/DATA_ATTRIBUTION.md
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curl -L -o DATA_ATTRIBUTION.md https://huggingface.co/DesaiJayM/Bharat-24B-Math-Code/resolve/main/DATA_ATTRIBUTION.md
1.09 kB
Question source attribution
- NuminaMath-TIR, Project Numina,2024. Authors: Jia Li, Edward Beeching, Lewis Tunstall, Ben Lipkin, Roman Soletskyi, Shengyi Costa Huang, Kashif Rasul, Longhui Yu, Albert Jiang, Ziju Shen, Zihan Qin, Bin Dong, Li Zhou, Yann Fleureau, Guillaume Lample, Stanislas Polu. Source: https://huggingface.co/datasets/AI-MO/NuminaMath-TIR . License: Apache2.0.
- OpenCodeInstruct, NVIDIA Corporation,2025. Authors: Wasi Uddin Ahmad, Aleksander Ficek, Mehrzad Samadi, Jocelyn Huang, Vahid Noroozi, Somshubra Majumdar, Boris Ginsburg. Source: https://huggingface.co/datasets/nvidia/OpenCodeInstruct . Paper: https://arxiv.org/abs/2504.04030 . License: Creative Commons Attribution4.0 International: https://creativecommons.org/licenses/by/4.0/ .
Modification: selected question inputs were retained unchanged. The original dataset response fields were replaced with new synthetic response targets for the5,000-response fine-tuning run. Training records are not included in this release. No endorsement by the base-model or dataset authors is implied.