See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: huggyllama/llama-7b
bf16: true
chat_template: llama3
datasets:
- data_files:
  - 7a0dcf2cd449adfe_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/7a0dcf2cd449adfe_train_data.json
  type:
    field_input: context
    field_instruction: question
    field_output: answer
    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: lesso04/96a3ec2e-5d47-44a6-b6e3-d15a7faac8b0
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: 50
micro_batch_size: 8
mlflow_experiment_name: /tmp/7a0dcf2cd449adfe_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: true
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: b70af139-fd5f-4c53-91c7-aafc7cf0c6d0
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: b70af139-fd5f-4c53-91c7-aafc7cf0c6d0
warmup_steps: 10
weight_decay: 0.01
xformers_attention: false

96a3ec2e-5d47-44a6-b6e3-d15a7faac8b0

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

• Loss: nan

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
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• 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: 50

Training results

Training Loss	Epoch	Step	Validation Loss
0.0	0.0061	1	nan
0.0	0.0306	5	nan
0.0	0.0612	10	nan
0.0	0.0917	15	nan
0.0	0.1223	20	nan
0.0	0.1529	25	nan
0.0	0.1835	30	nan
0.0	0.2141	35	nan
0.0	0.2446	40	nan
0.0	0.2752	45	nan
0.0	0.3058	50	nan

Framework versions

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