adapter: lora
base_model: unsloth/Llama-3.2-1B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - dialogsum_train_data.json
  ds_type: json
  path: /workspace/input_data/dialogsum_train_data.json
  type:
    field_input: dialogue
    field_instruction: topic
    field_output: summary
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 2
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: masatochi/tuning-0e7533c8-cd9a-4e62-88b6-79aaf3bbe621
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.06
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 3
mlflow_experiment_name: /tmp/dialogsum_train_data.json
model_type: LlamaForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 5
save_strategy: steps
sequence_len: 4096
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
val_set_size: 0.05
wandb_entity: lkotbimehdi
wandb_mode: online
wandb_project: lko
wandb_run: miner_id_24
wandb_runid: 0e7533c8-cd9a-4e62-88b6-79aaf3bbe621
warmup_steps: 30
weight_decay: 0.0
xformers_attention: null

tuning-0e7533c8-cd9a-4e62-88b6-79aaf3bbe621

This model is a fine-tuned version of unsloth/Llama-3.2-1B-Instruct on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.0166

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: 3
eval_batch_size: 3
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 24
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 30
training_steps: 200

Training results

Training Loss	Epoch	Step	Validation Loss
2.5495	0.0018	1	2.3448
1.1668	0.0598	34	1.1669
0.9638	0.1196	68	1.0743
1.0765	0.1794	102	1.0453
1.1714	0.2392	136	1.0312
0.9518	0.2990	170	1.0166

Framework versions

PEFT 0.13.2
Transformers 4.45.2
Pytorch 2.4.1+cu124
Datasets 3.0.1
Tokenizers 0.20.1