metadata
license: bigcode-openrail-m
library_name: peft
tags:
- generated_from_trainer
base_model: aurora-m/aurora-m-v0.1
model-index:
- name: lora-out
results: []
See axolotl config
axolotl version: 0.4.0
base_model: aurora-m/aurora-m-v0.1 # this can be swapped for mdel model when the model is released
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
is_llama_derived_model: false
load_in_8bit: false # when this is true inference quality is terrible
load_in_4bit: false
strict: false
datasets:
- path: /workspace/axolotl-mdel/mtg.txt # change this to where your dataset is
type: completion # change this to 'alpaca' if you are using alpaca formatting
lora_modules_to_save:
- embed_tokens
- lm_head
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./lora-out
sequence_len: 4096 # this can be tweaked for efficiency
sample_packing: true
pad_to_sequence_len: true
adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project: mtg-aurora-experiement # give this a name
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 2 # this can be tweaked for efficiency
micro_batch_size: 1 # this can be tweaked for efficiency
num_epochs: 1 # this can be experimented with
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: true
group_by_length: false
bf16: true
fp16: false
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: false # when this is true, inference quality is terrible
s2_attention:
warmup_steps: 10 # this can be tweaked for efficiency
evals_per_epoch: 10 # this can be tweaked for efficiency
eval_table_size:
eval_table_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
pad_token: "<|endoftext|>"
eos_token: "<|endoftext|>"
lora-out
This model is a fine-tuned version of aurora-m/aurora-m-v0.1 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7934
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: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 4.2853 | 0.0 | 1 | 4.0866 |
| 2.188 | 0.1 | 25 | 1.9751 |
| 1.2702 | 0.21 | 50 | 1.2180 |
| 1.0671 | 0.31 | 75 | 1.0151 |
| 0.9542 | 0.41 | 100 | 0.9209 |
| 0.9318 | 0.52 | 125 | 0.8680 |
| 0.858 | 0.62 | 150 | 0.8284 |
| 0.8247 | 0.73 | 175 | 0.8080 |
| 0.8576 | 0.83 | 200 | 0.7966 |
| 0.9026 | 0.93 | 225 | 0.7934 |
Framework versions
- PEFT 0.8.2.dev0
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0