See axolotl config

axolotl version: 0.4.0

base_model: tomaszki/nous-twenty-nine
model_type: LlamaForCausalLM
is_llama_derived_model: true
hub_model_id: 29-0

load_in_8bit: false
load_in_4bit: false
strict: false


datasets:
  - path: tomaszki/nous-finetune-axolotl-completion-0
val_set_size: 0.0
output_dir: ./out

sequence_len: 512
sample_packing: false

wandb_project: axolotl
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.00
lora_target_linear: true
lora_fan_in_fan_out:

gradient_accumulation_steps: 1
micro_batch_size: 12
num_epochs: 1
optimizer: adamw_hf
lr_scheduler: cosine
learning_rate: 0.00002
cosine_min_lr_ratio: 0.5

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

gradient_checkpointing: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 0
saves_per_epoch: 1
debug:
deepspeed: #deepspeed_configs/zero2.json
weight_decay: 0.01
fsdp:
fsdp_config:
special_tokens:

29-0

This model is a fine-tuned version of tomaszki/nous-twenty-nine on an unknown 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: 12
• eval_batch_size: 12
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• num_epochs: 1

Training results

Framework versions

• PEFT 0.8.2
• Transformers 4.38.0.dev0
• Pytorch 2.1.2+cu118
• Datasets 2.17.0
• Tokenizers 0.15.0