--- library_name: transformers license: apache-2.0 base_model: HuggingFaceTB/SmolLM2-360M tags: - axolotl - generated_from_trainer datasets: - Emm9625/textwork-00-dedupe-0.75 - Emm9625/textwork-00-dedupe-0.75 - Emm9625/textwork-00-dedupe-0.75 model-index: - name: tw-350M-dedupe-0.75-overfit results: [] --- [Built with Axolotl](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config axolotl version: `0.6.0` ```yaml # Original base model config # base_model: Dans-DiscountModels/Meta-Llama-3.2-3B-ChatML # Using smaller model instead base_model: HuggingFaceTB/SmolLM2-360M # Original tokenizer config # tokenizer_config: Dans-DiscountModels/Meta-Llama-3.2-3B-ChatML # Using matching tokenizer for smaller model tokenizer_config: HuggingFaceTB/SmolLM2-360M # Model loading configuration load_in_8bit: false load_in_4bit: false strict: false # Chat template configuration chat_template: chatml # Dataset configuration datasets: - path: Emm9625/textwork-00-dedupe-0.75 name: smol-constraints split: train type: chat_template field_messages: messages message_field_role: role message_field_content: content train_on_eos: turn # shards: 2 # shard_idx: 0 - path: Emm9625/textwork-00-dedupe-0.75 name: smol-rewrite split: train type: chat_template field_messages: messages message_field_role: role message_field_content: content train_on_eos: turn # shards: 2 # shard_idx: 0 - path: Emm9625/textwork-00-dedupe-0.75 name: smol-summarize split: train type: chat_template field_messages: messages message_field_role: role message_field_content: content train_on_eos: turn # shards: 2 # shard_idx: 0 test_datasets: - path: Emm9625/textwork-00-dedupe-0.75 name: smol-constraints split: test type: chat_template field_messages: messages message_field_role: role message_field_content: content train_on_eos: turn shards: 5 shard_idx: 0 - path: Emm9625/textwork-00-dedupe-0.75 name: smol-rewrite split: test type: chat_template field_messages: messages message_field_role: role message_field_content: content train_on_eos: turn shards: 5 shard_idx: 0 - path: Emm9625/textwork-00-dedupe-0.75 name: smol-summarize split: test type: chat_template field_messages: messages message_field_role: role message_field_content: content train_on_eos: turn shards: 5 shard_idx: 0 dataset_prepared_path: /notebooks/last_run_prepared output_dir: /tmp/meow/ hub_model_id: Emm9625/tw-350M-dedupe-0.75-overfit hub_strategy: checkpoint # Whether to use hf `use_auth_token` for loading datasets. Useful for fetching private datasets # Required to be true when used in combination with `push_dataset_to_hub` hf_use_auth_token: true # Model configuration sequence_len: 4096 sample_packing: true eval_sample_packing: true pad_to_sequence_len: true adapter: lora_model_dir: lora_r: 16 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: lora_target_modules: - gate_proj - down_proj - up_proj - q_proj - v_proj - k_proj - o_proj # Unsloth optimizations unsloth_cross_entropy_loss: true unsloth_rms_norm: true unsloth_rope: true #Lora Optimizations # unsloth_lora_mlp: true # unsloth_lora_qkv: true # unsloth_lora_o: true # Training configuration gradient_accumulation_steps: 1 micro_batch_size: 32 num_epochs: 5 optimizer: adamw_8bit lr_scheduler: cosine learning_rate: 1e-5 torch_compile: auto train_on_inputs: false group_by_length: false bf16: true gradient_checkpointing: true flash_attention: true # Training monitoring loss_watchdog_threshold: 5.0 loss_watchdog_patience: 3 warmup_ratio: 0.10 weight_decay: 0.00 saves_per_epoch: 1 evals_per_epoch: 5 save_safetensors: true wandb_project: textwork-00-dedupe logging_steps: 1 # Special tokens configuration special_tokens: eos_token: "<|im_end|>" bos_token: "<|im_start|>" pad_token: "<|im_end|>" fsdp: fsdp_config: ```

# tw-350M-dedupe-0.75-overfit This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-360M](https://huggingface.co/HuggingFaceTB/SmolLM2-360M) on the Emm9625/textwork-00-dedupe-0.75, the Emm9625/textwork-00-dedupe-0.75 and the Emm9625/textwork-00-dedupe-0.75 datasets. It achieves the following results on the evaluation set: - Loss: 1.2812 ## 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: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_8BIT 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: 92 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.5664 | 0.0054 | 1 | 1.5880 | | 1.5605 | 0.2 | 37 | 1.5856 | | 1.6023 | 0.4 | 74 | 1.5722 | | 1.5213 | 0.6 | 111 | 1.5354 | | 1.4658 | 0.8 | 148 | 1.4970 | | 1.4403 | 1.0 | 185 | 1.4584 | | 1.4088 | 1.1946 | 222 | 1.4166 | | 1.3583 | 1.3946 | 259 | 1.3798 | | 1.3049 | 1.5946 | 296 | 1.3495 | | 1.3063 | 1.7946 | 333 | 1.3293 | | 1.2535 | 1.9946 | 370 | 1.3154 | | 1.2862 | 2.1892 | 407 | 1.3059 | | 1.3075 | 2.3892 | 444 | 1.2983 | | 1.26 | 2.5892 | 481 | 1.2935 | | 1.241 | 2.7892 | 518 | 1.2896 | | 1.2975 | 2.9892 | 555 | 1.2864 | | 1.264 | 3.1838 | 592 | 1.2843 | | 1.2527 | 3.3838 | 629 | 1.2832 | | 1.2438 | 3.5838 | 666 | 1.2822 | | 1.3144 | 3.7838 | 703 | 1.2814 | | 1.2393 | 3.9838 | 740 | 1.2815 | | 1.2786 | 4.1784 | 777 | 1.2809 | | 1.2307 | 4.3784 | 814 | 1.2811 | | 1.2784 | 4.5784 | 851 | 1.2811 | | 1.3118 | 4.7784 | 888 | 1.2810 | | 1.3135 | 4.9784 | 925 | 1.2812 | ### Framework versions - Transformers 4.47.1 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0