See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 055a96136a6f31e2_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/055a96136a6f31e2_train_data.json
  type:
    field_input: filename
    field_instruction: title
    field_output: text
    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: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: dimasik1987/6c47943b-6b6c-42e5-a267-d92e7a254c4e
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
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: 30
micro_batch_size: 2
mlflow_experiment_name: /tmp/055a96136a6f31e2_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
special_tokens:
  pad_token: <|eot_id|>
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: 6c47943b-6b6c-42e5-a267-d92e7a254c4e
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 6c47943b-6b6c-42e5-a267-d92e7a254c4e
warmup_steps: 10
weight_decay: 0.01
xformers_attention: true

6c47943b-6b6c-42e5-a267-d92e7a254c4e

This model is a fine-tuned version of VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.9052

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: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 4
• total_train_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: 30

Training results

Training Loss	Epoch	Step	Validation Loss
2.107	0.0004	1	2.0542
2.117	0.0012	3	2.0532
2.0064	0.0024	6	2.0407
2.102	0.0035	9	2.0079
2.0705	0.0047	12	1.9765
1.9314	0.0059	15	1.9411
1.8998	0.0071	18	1.9271
1.7885	0.0083	21	1.9162
1.9157	0.0095	24	1.9090
1.9426	0.0106	27	1.9058
1.7657	0.0118	30	1.9052

Framework versions

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