See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: NousResearch/Nous-Hermes-llama-2-7b
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - dce1f45ccc949531_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/dce1f45ccc949531_train_data.json
  type:
    field_input: Tweet
    field_instruction: text_0
    field_output: Stance
    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: true
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: sn56/18eac61e-1856-417a-89be-c9dc382b3c57
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: false
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/dce1f45ccc949531_train_data.json
model_type: MistralForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
sample_packing: false
saves_per_epoch: 4
sequence_len: 4056
strict: false
tf32: false
tokenizer_type: LlamaTokenizer
train_on_inputs: false
val_set_size: 0.05
wandb_entity: null
wandb_mode: offline
wandb_name: 18eac61e-1856-417a-89be-c9dc382b3c57
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 18eac61e-1856-417a-89be-c9dc382b3c57
warmup_steps: 10
weight_decay: 0.0

18eac61e-1856-417a-89be-c9dc382b3c57

This model is a fine-tuned version of NousResearch/Nous-Hermes-llama-2-7b on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.2835

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: 0.0002
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• total_eval_batch_size: 8
• 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: 10

Training results

Training Loss	Epoch	Step	Validation Loss
4.7278	0.0079	1	4.7581
4.3878	0.0237	3	4.3996
2.3291	0.0473	6	1.2194
0.3618	0.0710	9	0.2835

Framework versions

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