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Model: shisa-ai/ablation-51-reasoning.mathcode-shisa-v2-llama-3.1-8b Source: Original Platform
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README.md
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README.md
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---
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library_name: transformers
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tags:
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- generated_from_trainer
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datasets:
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- shisa-ai/wip-tulu-3-math-code-all
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model-index:
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- name: outputs/ablation-51-reasoning.mathcode-shisa-v2-llama-3.1-8b
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
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<details><summary>See axolotl config</summary>
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axolotl version: `0.6.0`
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```yaml
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# train w/ shisa-ai/shisa-v1-athenev2-reannotated-filtered
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base_model: /fsx2/outputs/ablation-26-reasoning.inputs-shisa-v2-llama-3.1-8b-lr8e6
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model_type: LlamaForCausalLM
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tokenizer_type: AutoTokenizer
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load_in_8bit: false
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load_in_4bit: false
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strict: false
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# User Liger
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plugins:
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- axolotl.integrations.liger.LigerPlugin
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liger_rope: true
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liger_rms_norm: true
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liger_glu_activation: true
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liger_fused_linear_cross_entropy: true
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chat_template: llama3
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datasets:
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- path: shisa-ai/wip-tulu-3-math-code-all
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type: chat_template
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field_messages: conversations
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message_property_mappings:
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role: role
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content: content
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roles:
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system:
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- system
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assistant:
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- gpt
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- model
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- assistant
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user:
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- human
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- user
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roles_to_train: ["assistant"]
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dataset_prepared_path: last_run_prepared
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val_set_size: 0.05
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output_dir: ./outputs/ablation-51-reasoning.mathcode-shisa-v2-llama-3.1-8b
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sequence_len: 8192
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sample_packing: true
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pad_to_sequence_len: true
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# marginal difference
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neftune_noise_alpha: 5
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use_wandb: true
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wandb_project: shisa-v2
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wandb_entity: augmxnt
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wandb_name: ablation-51-reasoning.mathcode-shisa-v2-llama-3.1-8b
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gradient_accumulation_steps: 2
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micro_batch_size: 4
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num_epochs: 3
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optimizer: paged_adamw_8bit
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lr_scheduler: linear
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learning_rate: 8e-6
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train_on_inputs: false
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group_by_length: false
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bf16: auto
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fp16:
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tf32: false
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gradient_checkpointing: true
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gradient_checkpointing_kwargs:
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use_reentrant: false
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early_stopping_patience:
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resume_from_checkpoint:
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logging_steps: 1
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xformers_attention:
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flash_attention: true
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warmup_steps: 100
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evals_per_epoch: 2
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eval_table_size:
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saves_per_epoch: 0
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save_total_limit: 1 # Only store a single checkpoint
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debug:
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deepspeed: zero3_bf16.json
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weight_decay: 0.00
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fsdp:
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fsdp_config:
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special_tokens:
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pad_token: <|end_of_text|>
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```
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</details><br>
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# outputs/ablation-51-reasoning.mathcode-shisa-v2-llama-3.1-8b
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This model was trained from scratch on the shisa-ai/wip-tulu-3-math-code-all dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.9628
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 8e-06
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 8
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 64
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- total_eval_batch_size: 32
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- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 100
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- num_epochs: 3.0
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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| 0.742 | 0.0015 | 1 | 1.3548 |
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| 0.3478 | 0.5 | 338 | 0.9478 |
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| 0.3349 | 1.0 | 676 | 0.9374 |
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| 0.306 | 1.5 | 1014 | 0.9428 |
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| 0.3196 | 2.0 | 1352 | 0.9382 |
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| 0.2741 | 2.5 | 1690 | 0.9638 |
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| 0.2784 | 3.0 | 2028 | 0.9628 |
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### Framework versions
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- Transformers 4.48.3
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- Pytorch 2.6.0+cu124
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- Datasets 3.2.0
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- Tokenizers 0.21.0
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