See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: unsloth/SmolLM-135M-Instruct
bf16: true
chat_template: llama3
datasets:
- data_files:
  - a761b57816c8798d_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/a761b57816c8798d_train_data.json
  type:
    field_input: input
    field_instruction: instruction
    field_output: output_1
    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: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: false
hub_model_id: lesso11/5269e253-7a86-4bb0-b886-b46cbe7b732a
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_memory:
  0: 77GiB
max_steps: 50
micro_batch_size: 8
mlflow_experiment_name: /tmp/a761b57816c8798d_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: 25
save_strategy: steps
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: true
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: be00a9a0-65e6-4c81-983e-87f2acc483cb
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: be00a9a0-65e6-4c81-983e-87f2acc483cb
warmup_steps: 10
weight_decay: 0.01
xformers_attention: false

5269e253-7a86-4bb0-b886-b46cbe7b732a

This model is a fine-tuned version of unsloth/SmolLM-135M-Instruct 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: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• 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: 50

Training results

Training Loss	Epoch	Step	Validation Loss
0.0	0.0034	1	nan
0.0	0.0170	5	nan
0.0	0.0340	10	nan
0.0	0.0509	15	nan
0.0	0.0679	20	nan
0.0	0.0849	25	nan
0.0	0.1019	30	nan
0.0	0.1188	35	nan
0.0	0.1358	40	nan
0.0	0.1528	45	nan
0.0	0.1698	50	nan

Framework versions

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