See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: openlm-research/open_llama_3b
bf16: true
chat_template: llama3
datasets:
- data_files:
  - d7c287339d6d7874_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/d7c287339d6d7874_train_data.json
  type:
    field_input: input
    field_instruction: question
    field_output: output
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    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: 4
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: false
hub_model_id: lesso02/cc06f8a4-4588-4595-bf2b-d3d25c761247
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 1.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_memory:
  0: 70GiB
max_steps: 25
micro_batch_size: 4
mlflow_experiment_name: /tmp/d7c287339d6d7874_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 20
save_strategy: steps
sequence_len: 1024
special_tokens:
  pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: cc06f8a4-4588-4595-bf2b-d3d25c761247
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: cc06f8a4-4588-4595-bf2b-d3d25c761247
warmup_steps: 5
weight_decay: 0.01
xformers_attention: false

cc06f8a4-4588-4595-bf2b-d3d25c761247

This model is a fine-tuned version of openlm-research/open_llama_3b on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.4956

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: 1e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 8
• optimizer: Use OptimizerNames.ADAMW_TORCH 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: 5
• training_steps: 25

Training results

Training Loss	Epoch	Step	Validation Loss
1.6696	0.0009	1	1.5766
1.7193	0.0035	4	1.5750
1.8504	0.0071	8	1.5601
1.6983	0.0106	12	1.5368
1.5909	0.0142	16	1.5126
1.6391	0.0177	20	1.4992
1.7554	0.0213	24	1.4956

Framework versions

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