metadata

library_name: transformers
base_model: trl-internal-testing/tiny-random-LlamaForCausalLM
tags:
  - axolotl
  - generated_from_trainer
model-index:
  - name: dab16ec4-4ddf-4ee5-8888-3dc2a83f0f86
    results: []

See axolotl config

axolotl version: 0.4.1

base_model: trl-internal-testing/tiny-random-LlamaForCausalLM
batch_size: 32
bf16: true
chat_template: tokenizer_default_fallback_alpaca
datasets:
- data_files:
  - f4a61305a746447c_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/f4a61305a746447c_train_data.json
  type:
    field_instruction: sentence1
    field_output: sentence2
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
eval_steps: 20
flash_attention: true
gpu_memory_limit: 80GiB
gradient_checkpointing: true
group_by_length: true
hub_model_id: willtensora/dab16ec4-4ddf-4ee5-8888-3dc2a83f0f86
hub_strategy: checkpoint
learning_rate: 0.0002
logging_steps: 10
lr_scheduler: cosine
max_steps: 2500
micro_batch_size: 4
model_type: AutoModelForCausalLM
optimizer: adamw_bnb_8bit
output_dir: /workspace/axolotl/configs
pad_to_sequence_len: true
resize_token_embeddings_to_32x: false
sample_packing: false
save_steps: 40
save_total_limit: 1
sequence_len: 2048
tokenizer_type: LlamaTokenizerFast
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.1
wandb_entity: ''
wandb_mode: online
wandb_name: trl-internal-testing/tiny-random-LlamaForCausalLM-/workspace/input_data/f4a61305a746447c_train_data.json
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: default
warmup_ratio: 0.05
xformers_attention: true

dab16ec4-4ddf-4ee5-8888-3dc2a83f0f86

This model is a fine-tuned version of trl-internal-testing/tiny-random-LlamaForCausalLM on the None 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: 0.0002
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_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
training_steps: 13

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.01	1	10.3686

Framework versions

Transformers 4.46.0
Pytorch 2.5.0+cu124
Datasets 3.0.1
Tokenizers 0.20.1