open-llama-3.2-1B-Instruct

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open-llama-Instruct

• This model is a fine-tuned version of meta-llama/Llama-3.2-1B-Instruct on the diabolic6045/OpenHermes-2.5_alpaca_10 dataset. which is 10% of OpenHermes 2.5 Dataset

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• total_train_batch_size: 4
• total_eval_batch_size: 4
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 10
• num_epochs: 1
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.45.2
• Pytorch 2.1.2
• Datasets 3.0.1
• Tokenizers 0.20.1

<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 axolotl config</summary>

axolotl version: 0.4.1

base_model: meta-llama/Llama-3.2-1B-Instruct

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: diabolic6045/OpenHermes-2.5_alpaca_10
    type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0
output_dir: ./outputs/out
hub_model_id: diabolic6045/open-llama-Instruct
hf_use_auth_token: true

sequence_len: 1024
sample_packing: true
pad_to_sequence_len: true

wandb_project: open-llama
wandb_entity: 
wandb_watch: all
wandb_name: open-llama
wandb_log_model: 

gradient_accumulation_steps: 1
micro_batch_size: 2
num_epochs: 1

optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5

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

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

warmup_steps: 10
evals_per_epoch: 2
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|end_of_text|>

</details><br>