See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: HuggingFaceH4/tiny-random-LlamaForCausalLM
bf16: true
chat_template: llama3
datasets:
- data_files:
  - 1af03003d135629c_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/1af03003d135629c_train_data.json
  type:
    field_instruction: question
    field_output: answerKey
    format: '{instruction}'
    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: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: false
hub_model_id: sn56a1/4dee6e01-3061-4ce5-8ac7-b2660fba13f2
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
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: 77GiB
max_steps: 100
micro_batch_size: 8
mlflow_experiment_name: /tmp/1af03003d135629c_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
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: 25
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: disabled
wandb_name: 4dee6e01-3061-4ce5-8ac7-b2660fba13f2
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 4dee6e01-3061-4ce5-8ac7-b2660fba13f2
warmup_steps: 10
weight_decay: 0.01
xformers_attention: false

4dee6e01-3061-4ce5-8ac7-b2660fba13f2

This model is a fine-tuned version of HuggingFaceH4/tiny-random-LlamaForCausalLM on the None dataset. It achieves the following results on the evaluation set:

• Loss: 10.3255

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: 0.0001
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• total_eval_batch_size: 32
• 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: 10
• training_steps: 97

Training results

Training Loss	Epoch	Step	Validation Loss
10.3769	0.0308	1	10.3866
10.4226	0.2769	9	10.3843
10.4181	0.5538	18	10.3782
10.3612	0.8308	27	10.3712
9.8388	1.1077	36	10.3627
8.42	1.3846	45	10.3533
9.4193	1.6615	54	10.3438
11.6986	1.9385	63	10.3358
10.3342	2.2154	72	10.3297
10.3571	2.4923	81	10.3266
10.3199	2.7692	90	10.3255

Framework versions

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