See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: NousResearch/Hermes-2-Pro-Llama-3-8B
bf16: true
chat_template: llama3
cosine_min_lr_ratio: 0.3
dataset_prepared_path: null
datasets:
- data_files:
  - f33541f0ebd16cfb_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/f33541f0ebd16cfb_train_data.json
  type:
    field_input: input
    field_instruction: instruction
    field_output: output
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 4
eval_max_new_tokens: 128
eval_steps: 200
eval_table_size: null
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: Romain-XV/746dd9e3-5a88-4464-839e-1e676857ad59
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_best_model_at_end: true
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 683
micro_batch_size: 4
mlflow_experiment_name: /tmp/f33541f0ebd16cfb_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: 200
sequence_len: 2048
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.04
wandb_entity: null
wandb_mode: online
wandb_name: 384ba855-73db-4c3b-bd2e-3034e5a2c7cb
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 384ba855-73db-4c3b-bd2e-3034e5a2c7cb
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

746dd9e3-5a88-4464-839e-1e676857ad59

This model is a fine-tuned version of NousResearch/Hermes-2-Pro-Llama-3-8B on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.0473

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: 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: 683

Training results

Training Loss	Epoch	Step	Validation Loss
1.5841	0.0003	1	1.7843
1.1903	0.0654	200	1.1948
0.9578	0.1308	400	1.1033
1.2471	0.1962	600	1.0473

Framework versions

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