See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: jingyeom/seal3.1.6n_7b
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - a60fcbc34afc57c6_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/a60fcbc34afc57c6_train_data.json
  type:
    field_instruction: prompt
    field_output: response
    format: '{instruction}'
    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
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: oldiday/3f0e331d-46a1-4130-a3c2-c14eca316b25
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: 0
logging_steps: 3
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_steps: 100
micro_batch_size: 8
mlflow_experiment_name: /tmp/a60fcbc34afc57c6_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
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: techspear-hub
wandb_mode: online
wandb_name: 50c81356-a88f-4540-8023-a75d19afd5ca
wandb_project: Gradients-On-Six
wandb_run: your_name
wandb_runid: 50c81356-a88f-4540-8023-a75d19afd5ca
warmup_steps: 10
weight_decay: 0.01
xformers_attention: null

3f0e331d-46a1-4130-a3c2-c14eca316b25

This model is a fine-tuned version of jingyeom/seal3.1.6n_7b on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.0705

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: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• 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: 100

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0001	1	1.5517
1.5138	0.0008	9	1.4811
1.2236	0.0015	18	1.3133
1.2256	0.0023	27	1.2074
1.2831	0.0031	36	1.1547
1.122	0.0038	45	1.1196
1.0719	0.0046	54	1.0973
0.9938	0.0053	63	1.0844
1.2025	0.0061	72	1.0764
1.1513	0.0069	81	1.0724
1.0705	0.0076	90	1.0708
1.0546	0.0084	99	1.0705

Framework versions

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