See axolotl config
axolotl version: 0.4.1
base_model: Aculi/Tinyllama-2B
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: ./datas/1.json
type: alpaca
- path: ./datas/2.json
type: alpaca
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs/thinking-tiny-llama
adapter: qlora
lora_model_dir:
sequence_len: 4096
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention: false
flash_attention: true
warmup_steps: 10
evals_per_epoch: 2
saves_per_epoch: 2
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
outputs/thinking-tiny-llama
This model is a fine-tuned version of Aculi/Tinyllama-2B on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0222
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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- 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 |
|---|---|---|---|
| 1.5625 | 0.0013 | 1 | 1.5692 |
| 1.1161 | 0.5002 | 400 | 1.0995 |
| 1.0509 | 1.0003 | 800 | 1.0633 |
| 1.0665 | 1.4867 | 1200 | 1.0422 |
| 1.012 | 1.9869 | 1600 | 1.0287 |
| 1.0124 | 2.4733 | 2000 | 1.0250 |
| 0.8544 | 2.9734 | 2400 | 1.0212 |
| 0.9435 | 3.4605 | 2800 | 1.0222 |
Framework versions
- PEFT 0.11.1
- Transformers 4.43.1
- Pytorch 2.3.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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