See axolotl config

axolotl version: 0.10.0.dev0

adapter: lora
base_model: Qwen/Qwen3-4B-Base
bf16: auto
chat_template: llama3
dataloader_num_workers: 12
dataset_prepared_path: null
datasets:
- data_files:
  - 0a204b49682b086c_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/
  type:
    field_input: functions
    field_instruction: user
    field_output: function_call
    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_steps: null
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: false
group_by_length: false
hub_model_id: aleegis/d84a3e46-674e-4f81-a1ce-36e9debaab47
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: null
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: constant
max_grad_norm: 1
max_steps: 800
micro_batch_size: 4
mlflow_experiment_name: /tmp/0a204b49682b086c_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 15
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: null
save_total_limit: 10
saves_per_epoch: 0
sequence_len: 2048
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.0
wandb_entity: null
wandb_mode: online
wandb_name: 65a4c4b0-7d88-4322-b76c-31e96c9f61c5
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 65a4c4b0-7d88-4322-b76c-31e96c9f61c5
warmup_steps: 80
weight_decay: 0
xformers_attention: null

d84a3e46-674e-4f81-a1ce-36e9debaab47

This model is a fine-tuned version of Qwen/Qwen3-4B-Base on an unknown dataset.

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: 2
• total_train_batch_size: 8
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: constant
• lr_scheduler_warmup_steps: 80
• training_steps: 800

Training results

Framework versions

• PEFT 0.15.2
• Transformers 4.51.3
• Pytorch 2.5.1+cu124
• Datasets 3.5.1
• Tokenizers 0.21.1