Fischerboot/qlora-2b-thinking-full

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