See axolotl config
axolotl version: 0.4.1
adapter: qlora
base_model: Trisert/tinyllama-alpaca
bf16: false
dataset_prepared_path: null
datasets:
- ds_tipe: json
path: /content/instruct_dataset.jsonl
type: alpaca
debug: null
deepspeed: null
early_stopping_patience: null
eval_sample_packing: false
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
learning_rate: 0.0002
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
micro_batch_size: 8
model_type: AutoModelForCausalLM
num_epochs: 4
optimizer: paged_adamw_32bit
output_dir: ./outputs/qlora-out
pad_to_sequence_len: false
resume_from_checkpoint: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 4096
special_tokens: null
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
val_set_size: 0.05
wandb_entity: null
wandb_log_model: null
wandb_name: null
wandb_project: null
wandb_watch: null
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
outputs/qlora-out
This model is a fine-tuned version of Trisert/tinyllama-alpaca on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.0721
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
3.1589 | 0.0336 | 1 | 3.2144 |
2.8091 | 0.2689 | 8 | 2.6286 |
2.312 | 0.5378 | 16 | 2.2424 |
2.0133 | 0.8067 | 24 | 2.1532 |
2.1417 | 1.0756 | 32 | 2.1121 |
2.0591 | 1.3445 | 40 | 2.0889 |
2.0986 | 1.6134 | 48 | 2.0764 |
2.0055 | 1.8824 | 56 | 2.0758 |
1.8986 | 2.1513 | 64 | 2.0703 |
1.9346 | 2.4202 | 72 | 2.0701 |
2.0248 | 2.6891 | 80 | 2.0725 |
2.0656 | 2.9580 | 88 | 2.0726 |
1.8457 | 3.2269 | 96 | 2.0722 |
2.0257 | 3.4958 | 104 | 2.0721 |
1.936 | 3.7647 | 112 | 2.0721 |
Framework versions
- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.1.2+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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