yentinglin/Mistral-Small-24B-Instruct-2501-reasoning
Mistral-Small-Reasoning
<!-- Provide a quick summary of what the model is/does. -->
This model is a fine-tuned version of mistralai/Mistral-Small-24B-Instruct-2501, specifically optimized for mathematical reasoning tasks. It has been fine-tuned on datasets including OpenR1-Math-220k, and s1K-1.1, aiming to enhance its reasoning capabilities.
Model Details
Model Description
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- Developed by: Yenting Lin
- Funded by: Ubitus
- Model type: Instruction-tuned language model for reasoning
- Language(s) (NLP): English (en)
- License: Apache 2.0
- Finetuned from model: mistralai/Mistral-Small-24B-Instruct-2501
How to Get Started with the Model
A demo is available at twllm.com, and inference can be run using vLLM or sglang.
Training Details
The model was trained using 4×8 H100 GPUs, provided by **Ubitus**.
<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/> <details><summary>See Training config</summary>
axolotl version: `a98526ef7843a3e8aa006f260e6b4fb8912b5f1a`
base_model: mistralai/Mistral-Small-24B-Instruct-2501
plugins:
- axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: true
datasets:
- path: yentinglin/s1K-1.1-trl-format
type: chat_template
chat_template: tokenizer_default
field_messages: messages
message_field_role: role
message_field_content: content
- path: open-r1/OpenR1-Math-220k
type: chat_template
chat_template: tokenizer_default
field_messages: messages
message_field_role: from
message_field_content: value
dataset_prepared_path:
val_set_size: 0.0
output_dir: ./placeholder/
sequence_len: 32768
sample_packing: true
eval_sample_packing: False
pad_to_sequence_len: true
wandb_project: Reasoning
wandb_entity:
wandb_watch:
wandb_name: Mistral-24B-SFT-220k
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 5
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 2e-5
train_on_inputs: false
group_by_length: false
bf16: auto
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
logging_steps: 1
flash_attention: true
warmup_ratio: 0.1
saves_per_epoch: 2
weight_decay: 0.0
deepspeed: deepspeed_configs/zero3_bf16.json
special_tokens:
pad_token: "<pad>"</details><br>
Evaluation
The evaluation code is available at Hugging Face Open-R1. Note that I have updated the AIME 25 dataset to the full set, available at AIME 2025.
Our results below are averaged over multiple runs. See our eval details here.
Citation
If you use this model, please cite:
@article{yentinglin2025_mistral_reasoning,
author = {Yenting Lin},
title = {Mistral-Small-24B-Instruct-2501-reasoning},
journal = {Hugging Face},
year = {2025},
url = {https://huggingface.co/yentinglin/Mistral-Small-24B-Instruct-2501-reasoning}
}Disclaimer
This model is provided “as‑is” and without warranties of any kind. Users are solely responsible for evaluating the accuracy and suitability of the outputs. The developers assume no liability for any direct or indirect damages arising from its use. The model is strictly not intended for high‑risk applications such as medical diagnosis, legal advice, or financial investment. For such use cases, please consult qualified professionals.
本模型「如是」（as‑is）提供，使用者須自行評估結果之正確性與適用性。開發者對於使用本模型所引發之任何直接或間接損失，不承擔任何法律責任。 嚴禁用於醫療診斷、法律諮詢、金融投資等高風險場景；若有相關需求，請尋求專業人員協助。