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:
  - 0425fe5f1b5f3ce6_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/0425fe5f1b5f3ce6_train_data.json
  type:
    field_input: ctx
    field_instruction: activity_label
    field_output: label
    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: true
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: ardaspear/7f490964-9d81-4e29-a2a7-e92e9f65229b
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: 0
logging_steps: 3
lora_alpha: 128
lora_dropout: 0.1
lora_fan_in_fan_out: true
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_memory:
  0: 72GB
max_steps: 100
micro_batch_size: 4
mlflow_experiment_name: /tmp/0425fe5f1b5f3ce6_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: false
sample_packing: false
saves_per_epoch: 4
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: leixa-personal
wandb_mode: online
wandb_name: 7f490964-9d81-4e29-a2a7-e92e9f65229b
wandb_project: Gradients-On-Two
wandb_run: your_name
wandb_runid: 7f490964-9d81-4e29-a2a7-e92e9f65229b
warmup_steps: 10
weight_decay: 0.01
xformers_attention: null

7f490964-9d81-4e29-a2a7-e92e9f65229b

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: 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: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 4
• 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
No log	0.0008	1	nan
0.0	0.0076	9	nan
0.0	0.0153	18	nan
0.0	0.0229	27	nan
0.0	0.0305	36	nan
0.0	0.0382	45	nan
0.0	0.0458	54	nan
0.0	0.0534	63	nan
0.0	0.0611	72	nan
0.0	0.0687	81	nan
0.0	0.0763	90	nan
0.0	0.0840	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