--- library_name: transformers license: apache-2.0 base_model: unsloth/SmolLM-135M tags: - axolotl - generated_from_trainer model-index: - name: 09370687-f28e-45e5-91f6-f87011850a94 results: [] --- [Built with Axolotl](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config axolotl version: `0.4.1` ```yaml base_model: unsloth/SmolLM-135M batch_size: 32 bf16: true chat_template: tokenizer_default_fallback_alpaca datasets: - data_files: - 658988857b0a29c9_train_data.json ds_type: json format: custom path: /workspace/input_data/658988857b0a29c9_train_data.json type: field_input: choices field_instruction: subject field_output: question format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' eval_steps: 20 flash_attention: true gpu_memory_limit: 80GiB gradient_checkpointing: true group_by_length: true hub_model_id: willtensora/09370687-f28e-45e5-91f6-f87011850a94 hub_strategy: checkpoint learning_rate: 0.0002 logging_steps: 10 lr_scheduler: cosine max_steps: 2500 micro_batch_size: 4 model_type: AutoModelForCausalLM optimizer: adamw_bnb_8bit output_dir: /workspace/axolotl/configs pad_to_sequence_len: true resize_token_embeddings_to_32x: false sample_packing: false save_steps: 40 save_total_limit: 1 sequence_len: 2048 tokenizer_type: GPT2TokenizerFast train_on_inputs: false trust_remote_code: true val_set_size: 0.1 wandb_entity: '' wandb_mode: online wandb_name: unsloth/SmolLM-135M-/workspace/input_data/658988857b0a29c9_train_data.json wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: default warmup_ratio: 0.05 xformers_attention: true ```

# 09370687-f28e-45e5-91f6-f87011850a94 This model is a fine-tuned version of [unsloth/SmolLM-135M](https://huggingface.co/unsloth/SmolLM-135M) on the None 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 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 32 - total_eval_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 - training_steps: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 0.5 | 1 | 2.4449 | ### Framework versions - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1