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liminerity/
mm4.7-3b-slerp-comp-alpaca

Name: mm4.7-3b-slerp-comp-alpaca
Availability: InStock

peft

pytorch

llama

generated_from_trainer

liminerity/mm4.7-3b-slerp

adapter

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See axolotl config

axolotl version: 0.4.0

yaml

adapter: qlora
base_model: qlora-out2/merged
bf16: false
dataset_prepared_path: null
datasets:
- data_files:
  - Chain-of-Thought/formatted_cot_data/gsm8k_train.json
  path: QingyiSi/Alpaca-CoT
  type: alpaca:chat
debug: null
deepspeed: null
early_stopping_patience: null
evals_per_epoch: null
flash_attention: false
fp16: true
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
group_by_length: false
is_llama_derived_model: true
learning_rate: 0.0003
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lora_target_modules: null
lr_scheduler: cosine
max_steps: 369
micro_batch_size: 1
mlflow_experiment_name: colab-example
model_type: LlamaForCausalLM
num_epochs: 6
optimizer: paged_adamw_32bit
output_dir: ./qlora-out
pad_to_sequence_len: true
resume_from_checkpoint: null
sample_packing: true
saves_per_epoch: null
sequence_len: 2048
special_tokens:
  bos_token: <s>
  eos_token: </s>
  unk_token: <unk>
strict: false
tf32: false
tokenizer_type: LlamaTokenizer
train_on_inputs: true
val_set_size: 0.2
wandb_entity: null
wandb_log_model: null
wandb_name: null
wandb_project: null
wandb_watch: null
warmup_steps: 10
weight_decay: 0.1
xformers_attention: false

qlora-out

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.7597

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: 0.0003
train_batch_size: 1
eval_batch_size: 1
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
training_steps: 369
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
0.7414	0.59	369	0.7597

Framework versions

PEFT 0.8.2
Transformers 4.39.0.dev0
Pytorch 2.1.1+cu121
Datasets 2.17.1
Tokenizers 0.15.0

liminerity/mm4.7-3b-slerp-comp-alpaca

qlora-out

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

liminerity/
mm4.7-3b-slerp-comp-alpaca