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Fischerboot/
2b-lora-adapter-llama-alpaca

Name: 2b-lora-adapter-llama-alpaca
Availability: InStock

peft

llama

generated_from_trainer

Fischerboot/2b-tiny-llama-alpaca-instr

adapter

4-bit

bitsandbytes

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See axolotl config

axolotl version: 0.4.1

yaml

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

Fischerboot/2b-lora-adapter-llama-alpaca

outputs/24r

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Fischerboot/
2b-lora-adapter-llama-alpaca