See axolotl config
axolotl version: 0.4.1
base_model: Fischerboot/2b-tiny-llama-alpaca-instr
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: Fischerboot/freedom-rp-alpaca-shortend
type: alpaca
- path: diffnamehard/toxic-dpo-v0.1-NoWarning-alpaca
type: alpaca
- path: Fischerboot/alpaca-undensored-fixed-50k
type: alpaca
- path: Fischerboot/DAN-alpaca
type: alpaca
- path: Fischerboot/rp-alpaca-next-oone
type: alpaca
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs/24r
adapter: qlora
lora_model_dir:
sequence_len: 2048
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:
flash_attention: true
warmup_steps: 10
evals_per_epoch: 2
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
outputs/24r
This model is a fine-tuned version of Fischerboot/2b-tiny-llama-alpaca-instr on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.8245
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 |
|---|---|---|---|
| 1.7881 | 0.0017 | 1 | 2.5329 |
| 1.6899 | 0.4996 | 287 | 1.9272 |
| 1.5511 | 0.9991 | 574 | 1.8750 |
| 1.4797 | 1.4861 | 861 | 1.8476 |
| 1.5279 | 1.9856 | 1148 | 1.8270 |
| 1.4583 | 2.4726 | 1435 | 1.8275 |
| 1.5044 | 2.9721 | 1722 | 1.8215 |
| 1.3051 | 3.4582 | 2009 | 1.8243 |
| 1.5619 | 3.9578 | 2296 | 1.8245 |
Framework versions
- PEFT 0.11.1
- Transformers 4.43.1
- Pytorch 2.3.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
- Downloads last month
- 1