See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: NousResearch/CodeLlama-7b-hf-flash
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 260d0df94397bc03_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/260d0df94397bc03_train_data.json
  type:
    field_input: text
    field_instruction: question
    field_output: answer
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
ddp_find_unused_parameters: false
distributed_type: ddp
early_stopping_patience: null
env:
  CUDA_VISIBLE_DEVICES: 0,1
  MASTER_ADDR: localhost
  MASTER_PORT: '29500'
  NCCL_DEBUG: INFO
  NCCL_IB_DISABLE: '0'
  NCCL_P2P_DISABLE: '0'
  NCCL_P2P_LEVEL: NVL
  PYTORCH_CUDA_ALLOC_CONF: max_split_size_mb:512, garbage_collection_threshold:0.8
  WORLD_SIZE: '2'
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: true
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: true
hub_model_id: fats-fme/efab9aa1-4e65-4482-82f7-0799310b94b6
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: true
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_MB: 65000
max_steps: -1
micro_batch_size: 1
mlflow_experiment_name: /tmp/260d0df94397bc03_train_data.json
model_type: AutoModelForCausalLM
num_devices: 2
num_epochs: 1
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 2048
special_tokens:
  pad_token: </s>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: efab9aa1-4e65-4482-82f7-0799310b94b6
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: efab9aa1-4e65-4482-82f7-0799310b94b6
warmup_steps: 50
world_size: 2
xformers_attention: true

efab9aa1-4e65-4482-82f7-0799310b94b6

This model is a fine-tuned version of NousResearch/CodeLlama-7b-hf-flash on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0001

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: 1
• eval_batch_size: 1
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• gradient_accumulation_steps: 8
• total_train_batch_size: 16
• total_eval_batch_size: 2
• 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: 50
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
1.3568	0.0007	1	0.4650
0.0001	0.2503	337	0.0001
0.0001	0.5006	674	0.0002
0.0003	0.7508	1011	0.0001

Framework versions

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