5fd8fecc-6180-4524-8458-1a43fce628c5

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<details><summary>See axolotl config</summary>

axolotl version: 0.4.1

adapter: lora
base_model: Qwen/Qwen2.5-3B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - df9fd0b66f63a4eb_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/df9fd0b66f63a4eb_train_data.json
  type:
    field_instruction: instruct
    field_output: output
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
do_eval: true
eval_batch_size: 8
eval_max_new_tokens: 128
eval_steps: 567
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: Kromtao/5fd8fecc-6180-4524-8458-1a43fce628c5
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
local_rank: null
logging_steps: 50
lora_alpha: 16
lora_dropout: 0.1
lora_fan_in_fan_out: false
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 1134
micro_batch_size: 8
mlflow_experiment_name: /tmp/df9fd0b66f63a4eb_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: false
sample_packing: false
save_steps: 567
saves_per_epoch: null
seed: 9103
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: dc916161-c038-4d50-8932-641110bd4c02
wandb_project: kr03
wandb_run: your_name
wandb_runid: dc916161-c038-4d50-8932-641110bd4c02
warmup_steps: 100
weight_decay: 0.01
xformers_attention: true

</details><br>

5fd8fecc-6180-4524-8458-1a43fce628c5

This model is a fine-tuned version of Qwen/Qwen2.5-3B on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.7839

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: 8
• eval_batch_size: 8
• seed: 9103
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• 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: cosine
• lr_scheduler_warmup_steps: 100
• training_steps: 1134

Training results

Framework versions

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