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jtatman/
pythia-125m-gpt4-llm-cleaned

Name: pythia-125m-gpt4-llm-cleaned
Availability: InStock

generated_from_trainer

EleutherAI/pythia-160m-deduped-v0

adapter

apache-2.0

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See axolotl config

axolotl version: 0.4.1

yaml

base_model: EleutherAI/pythia-125m-deduped
load_in_8bit: false
datasets:
  - path: jtatman/storywriting_combined_instruct
    type: alpaca
dataset_prepared_path: ds-storytelling
val_set_size: 0.05
adapter: lora
lora_model_dir:
sequence_len: 512
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules:
  - query_key_value
lora_target_linear: true 
lora_fan_in_fan_out: true  # pythia/GPTNeoX lora specific
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
output_dir: ./outputs/lora-alpaca-pythia-125m
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 4
learning_rate: 0.00001
train_on_inputs: false
group_by_length: false
bf16: auto
tf32: false
float16: true
gpu_memory_limit: 8GiB
hub_model_id: jtatman/pythia-125m-gpt4-llm-cleaned 
lora_on_cpu: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
weight_decay: 0.1
evals_per_epoch: 4
logging_steps: 1

pythia-125m-gpt4-llm-cleaned

This model is a fine-tuned version of EleutherAI/pythia-125m-deduped on the None dataset. It achieves the following results on the evaluation set:

Loss: 2.6093

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: 1e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss
3.1847	0.0008	1	2.7080
2.8391	0.2504	326	2.6464
2.9722	0.5008	652	2.6312
2.9904	0.7512	978	2.6245
3.1704	1.0015	1304	2.6179
3.1108	1.2519	1630	2.6155
2.9321	1.5023	1956	2.6143
3.0499	1.7527	2282	2.6113
2.9776	2.0031	2608	2.6102
2.7773	2.2535	2934	2.6102
3.5383	2.5038	3260	2.6106
2.8105	2.7542	3586	2.6106
3.0778	3.0046	3912	2.6115
2.9706	3.2550	4238	2.6096
2.7671	3.5054	4564	2.6100
3.3049	3.7558	4890	2.6093

Framework versions

PEFT 0.11.1
Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1