adapter: qlora
auto_resume_from_checkpoints: false
base_model: scb10x/llama-3-typhoon-v1.5-8b-instruct
bf16: false
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - f61bec5b82911b5f_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/f61bec5b82911b5f_train_data.json
  type:
    field_input: genre
    field_instruction: premise
    field_output: hypothesis
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 4
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
flash_attention: true
fp16: true
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 32
gradient_checkpointing: true
group_by_length: false
hub_model_id: error577/52d32e8c-e6c5-404b-8326-b6845f7443fd
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: null
micro_batch_size: 1
mlflow_experiment_name: /tmp/f61bec5b82911b5f_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_torch_4bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 100
sequence_len: 128
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.0005
wandb_entity: null
wandb_mode: online
wandb_name: 0902982d-d12a-4780-a62c-f6980404ea63
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 0902982d-d12a-4780-a62c-f6980404ea63
warmup_steps: 30
weight_decay: 0.0
xformers_attention: null

52d32e8c-e6c5-404b-8326-b6845f7443fd

This model is a fine-tuned version of scb10x/llama-3-typhoon-v1.5-8b-instruct on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.8018

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: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 32
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH_4BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 30
num_epochs: 1
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
3.5146	0.0001	1	3.9565
1.8081	0.0078	100	1.7830
1.8802	0.0155	200	1.7761
1.585	0.0233	300	1.8033
1.6037	0.0311	400	1.7851
1.5661	0.0388	500	1.8069
1.8584	0.0466	600	1.8018

Framework versions

PEFT 0.13.2
Transformers 4.46.0
Pytorch 2.5.0+cu124
Datasets 3.0.1
Tokenizers 0.20.1