See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: fxmarty/tiny-llama-fast-tokenizer
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - fee8d932af6f9203_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/fee8d932af6f9203_train_data.json
  type:
    field_instruction: abstract
    field_output: title
    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: brixeus/a79f9523-6414-46a1-aa98-3069e72d3a1f
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: 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/fee8d932af6f9203_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: </s>
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: 1cfdf75e-d1c9-419a-b338-98971e8ecff0
wandb_project: Gradients-On-Three
wandb_run: your_name
wandb_runid: 1cfdf75e-d1c9-419a-b338-98971e8ecff0
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

a79f9523-6414-46a1-aa98-3069e72d3a1f

This model is a fine-tuned version of fxmarty/tiny-llama-fast-tokenizer on the None dataset. It achieves the following results on the evaluation set:

• Loss: 10.3732

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: 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	0.0129	1	10.3796
10.3772	0.1158	9	10.3793
10.3772	0.2315	18	10.3785
10.3761	0.3473	27	10.3777
10.3784	0.4630	36	10.3769
10.3767	0.5788	45	10.3760
10.3733	0.6945	54	10.3751
10.3714	0.8103	63	10.3743
10.3754	0.9260	72	10.3738
10.5007	1.0482	81	10.3734
10.4191	1.1640	90	10.3732
10.4156	1.2797	99	10.3732

Framework versions

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