👁 Built with Axolotl

DeepSeek-R1-Distill-Qwen-32B-axolotl-sft-v1.0

This model is a fine-tuned version of deepseek-ai/DeepSeek-R1-Distill-Qwen-32B on the kanhatakeyama/ramdom-to-fixed-multiturn-Calm3, the Aratako/Magpie-Tanuki-Qwen2.5-72B-Answered, the Aratako/magpie-qwen2.5-32b-reasoning-100k-formatted, the Aratako/magpie-reasoning-llama-nemotron-70b-100k-filtered, the Aratako/Open-Platypus-Japanese-masked-formatted, the kanhatakeyama/wizardlm8x22b-logical-math-coding-sft_additional-ja, the Aratako/magpie-ultra-v0.1-formatted, the Aratako/orca-agentinstruct-1M-v1-selected and the Aratako/Synthetic-JP-EN-Coding-Dataset-801k-50k datasets. It achieves the following results on the evaluation set: - Loss: 0.6154

以下、Axolotlの実行コード

!apt-get update
!apt-get install -y libopenmpi-dev

!git clone https://github.com/axolotl-ai-cloud/axolotl

cd axolotl
!pip install -e .
!pip install packaging ninja
!pip install flash-attn
!pip install deepspeed
!pip install mpi4py

# write権限のあるtokenを利用してHFにログイン（学習後のモデルアップロードに必要）
!huggingface-cli login --token WRITE ME
# wandbにログイン（wandbに学習ログを残したい場合）
!wandb login WRITE ME

import axolotl

!python -m axolotl.cli.preprocess /workspace/deepseek-32b-ver001-simpo.yml --debug

! accelerate launch -m axolotl.cli.train /workspace/deepseek-32b-ver001-simpo.yml --deepspeed deepspeed_configs/zero3_bf16.json

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0003
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• distributed_type: multi-GPU
• gradient_accumulation_steps: 16
• total_train_batch_size: 16
• optimizer: Use paged_adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 10
• num_epochs: 1.0

Training results

Framework versions

• PEFT 0.14.0
• Transformers 4.49.0
• Pytorch 2.5.1+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0

Base model

deepseek-ai/DeepSeek-R1-Distill-Qwen-32B

(220)