See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: OpenBuddy/openbuddy-llama2-13b-v8.1-fp16
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 24ad605fd0de4b3f_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/24ad605fd0de4b3f_train_data.json
  type:
    field_instruction: problem
    field_output: generated_solution
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 5
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 16
gradient_checkpointing: true
group_by_length: false
hub_model_id: tuantmdev/90f9f7ea-2ebc-4062-bd99-6e8554fad2b3
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 2e-05
load_in_4bit: false
load_in_8bit: true
local_rank: null
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 50
micro_batch_size: 2
mixed_precision: bf16
mlflow_experiment_name: /tmp/24ad605fd0de4b3f_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_strategy: best
saves_per_epoch: 5
sequence_len: 512
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: f179a7be-855a-427c-9f97-e9b9ba8fdb44
wandb_project: Gradients-On-Demand
wandb_run: unknown
wandb_runid: f179a7be-855a-427c-9f97-e9b9ba8fdb44
warmup_steps: 10
weight_decay: 0.01
xformers_attention: null

90f9f7ea-2ebc-4062-bd99-6e8554fad2b3

This model is a fine-tuned version of OpenBuddy/openbuddy-llama2-13b-v8.1-fp16 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: 2e-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 16
• total_train_batch_size: 32
• optimizer: Use OptimizerNames.ADAMW_BNB 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
No log	0.0015	1	nan
2103462092.8	0.0151	10	nan
163.8108	0.0302	20	nan
0.0067	0.0453	30	nan
238818741452.8	0.0604	40	nan
34634444.8	0.0755	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