See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: unsloth/Meta-Llama-3.1-8B-Instruct
bf16: true
chat_template: llama3
datasets:
- data_files:
  - 402e7684aac5d457_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/402e7684aac5d457_train_data.json
  type:
    field_input: options
    field_instruction: Problem
    field_output: correct
    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: lesso06/d2a775b7-cb7f-460d-bf93-2f13bf2184fa
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/402e7684aac5d457_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
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: d2a775b7-cb7f-460d-bf93-2f13bf2184fa
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: d2a775b7-cb7f-460d-bf93-2f13bf2184fa
warmup_steps: 10
weight_decay: 0.01
xformers_attention: false

d2a775b7-cb7f-460d-bf93-2f13bf2184fa

This model is a fine-tuned version of unsloth/Meta-Llama-3.1-8B-Instruct 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: 100

Training results

Training Loss	Epoch	Step	Validation Loss
0.0	0.0001	1	nan
0.0	0.0007	9	nan
0.0	0.0014	18	nan
0.0	0.0020	27	nan
0.0	0.0027	36	nan
0.0	0.0034	45	nan
0.0	0.0041	54	nan
0.0	0.0047	63	nan
0.0	0.0054	72	nan
0.0	0.0061	81	nan
0.0	0.0068	90	nan
0.0	0.0075	99	nan

Framework versions

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