base_model: deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct
trust_remote_code: true

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true

datasets:
  - path: axolotl_format_deepseek_combined_wm.json
    type: input_output
dataset_prepared_path: last_run_prepared_deepseek
    
output_dir: ./models/deepseek_wm
sequence_len: 4096

wandb_project: agent-v0
wandb_name: deepseek_wm

train_on_inputs: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_torch
learning_rate: 2e-5
xformers_attention:
flash_attention: true

logging_steps: 5

warmup_steps: 5
saves_per_epoch: 1
weight_decay: 0.0

deepspeed: axolotl/deepspeed_configs/zero3_bf16_cpuoffload_all.json

models/deepseek_wm

This model is a fine-tuned version of deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct on the axolotl_format_deepseek_combined_wm.json dataset.

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: 2e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 32
total_eval_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH 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: 5
num_epochs: 3

Training results

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu124
Datasets 3.1.0
Tokenizers 0.21.0

mfirth/agi-ds

models/deepseek_wm

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

mfirth/
agi-ds