axolotl-ai-co/kd-llama-1b-evolkit-distill-kd-ratio-0_4

See axolotl config

axolotl version: 0.6.0

base_model: meta-llama/Llama-3.2-1B
tokenizer_config: meta-llama/Llama-3.2-3B
# Automatically upload checkpoint and final model to HF
# hub_model_id: axolotl-ai-co/kd-llama-1b-evolkit-distill-ratio-0_4

plugins:
  - axolotl.integrations.kd.KDPlugin
  - axolotl.integrations.liger.LigerPlugin

liger_rms_norm: true
liger_glu_activation: true

torch_compile: true

strict: false

chat_template: llama3

kd_trainer: true
kd_ce_alpha: 0.6
kd_alpha: 0.4
kd_temperature: 1.0

dataloader_prefetch_factor: 256
dataloader_num_workers: 4
dataloader_pin_memory: true

gc_steps: -1  # gc at the end of each epoch

datasets:
- field_messages: messages_combined
  message_field_content: content
  message_field_role: role
  logprobs_field: llm_text_generation_vllm_logprobs
  path: winglian/evolkit-logprobs-pipeline-75k-v2
  type: axolotl.integrations.kd.chat_template
  split: train
  temperature: 1.0

dataset_prepared_path: last_run_prepared
val_set_size: 0.0
output_dir: ./outputs/out-1b-kd-more-saves

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

wandb_project: lobprob-kd-evolkit
wandb_entity: axolotl-ai
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 3e-5
save_safetensors: true

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: true

gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 100
evals_per_epoch: 2
eval_table_size:
saves_per_epoch: 20
debug:
# deepspeed: deepspeed_configs/zero1.json
weight_decay: 0.0
special_tokens:
  pad_token: <|finetune_right_pad_id|>
  eos_token: <|eot_id|>

outputs/out-1b-kd-more-saves

This model is a fine-tuned version of meta-llama/Llama-3.2-1B on the winglian/evolkit-logprobs-pipeline-75k-v2 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: 3e-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_FUSED 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: 100
• num_epochs: 3

Training results

Framework versions

• Transformers 4.48.1
• Pytorch 2.5.1+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0