mergekit

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<details><summary>See axolotl config</summary>

axolotl version: 0.4.1

base_model: Crystalcareai/Meta-llama-3.1-8b-instruct
bf16: 'True'
chat_template: chatml
dataset_prepared_path: ./sft_processed
dataset_processes: 12
datasets:
- field_messages: messages
  path: sft_dataset
  type: sharegpt
deepspeed: /axolotl/deepspeed_configs/zero3_bf16.json
eval_batch_size: 1
flash_attention: true
gradient_accumulation_steps: 8
gradient_checkpointing: true
learning_rate: 5.0e-06
logging_steps: 1
lr_scheduler: cosine
micro_batch_size: 2
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: data/sft-full
pad_to_sequence_len: true
sample_packing: true
save_safetensors: true
save_total_limit: 0
saves_per_epoch: 0
seed: 42
sequence_len: 4096
special_tokens:
  pad_token: <|end_of_text|>
tf32: false
tokens: []
use_tensorboard: true
val_set_size: 0

</details><br>

data/sft-full

This model is a fine-tuned version of Crystalcareai/Meta-llama-3.1-8b-instruct 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: 5e-06
• train_batch_size: 2
• eval_batch_size: 1
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• gradient_accumulation_steps: 8
• total_train_batch_size: 128
• total_eval_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• num_epochs: 3

Training results

Framework versions

• Transformers 4.43.1
• Pytorch 2.3.0+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1