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- axolotl/stratos.yaml +83 -0
- model-00001-of-00030.safetensors +1 -1
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- model-00003-of-00030.safetensors +1 -1
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- training_args.bin +3 -0
README.md
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---
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library_name: transformers
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license: llama3.1
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base_model: meta-llama/Llama-3.1-70B
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tags:
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- generated_from_trainer
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datasets:
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- bespokelabs/Bespoke-Stratos-17k
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model-index:
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- name: outputs/out/reasoning-70b-stratos
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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: `0.8.0.dev0`
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```yaml
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base_model: meta-llama/Llama-3.1-70B
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# Automatically upload checkpoint and final model to HF
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# hub_model_id: username/custom_model_name
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#
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plugins:
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- axolotl.integrations.liger.LigerPlugin
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- axolotl.integrations.spectrum.SpectrumPlugin
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spectrum_top_fraction: 0.5
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spectrum_model_name: meta-llama/Meta-Llama-3.1-70B
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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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strict: false
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chat_template: llama3
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datasets:
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- path: bespokelabs/Bespoke-Stratos-17k
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field_messages: conversations
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message_property_mappings:
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content: value
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role: from
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split: train
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type: chat_template
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dataset_prepared_path: last_run_prepared
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val_set_size: 0.0
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output_dir: ./outputs/out/reasoning-70b-stratos
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save_safetensors: true
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wandb_project: reasoning-70b-stratos
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wandb_entity: axolotl-ai
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wandb_watch:
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wandb_name:
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wandb_log_model:
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sequence_len: 16384
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sample_packing: true
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pad_to_sequence_len: true
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gradient_accumulation_steps: 1
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micro_batch_size: 4
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num_epochs: 3
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optimizer: adamw_torch_fused
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lr_scheduler: rex
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learning_rate: 5.0e-6
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embedding_lr: 1.0e-5
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max_grad_norm: 1.0
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train_on_inputs: false
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group_by_length: false
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bf16: true
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tf32: true
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gradient_checkpointing: offload
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gradient_checkpointing_kwargs:
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use_reentrant: true
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logging_steps: 1
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flash_attention: true
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warmup_steps: 20
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evals_per_epoch: 4
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saves_per_epoch: 1
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weight_decay: 0.01
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deepspeed: deepspeed_configs/zero3_bf16_cpuoffload_params.json
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special_tokens:
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pad_token: <|finetune_right_pad_id|>
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eos_token: <|eot_id|>
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added_tokens_overrides:
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128011: <think>
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128012: </think>
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128013: <|begin_of_thought|>
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128014: <|end_of_thought|>
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128015: <|begin_of_solution|>
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128016: <|end_of_solution|>
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fix_untrained_tokens:
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- 128011
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- 128012
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- 128013
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- 128014
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- 128015
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- 128016
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```
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</details><br>
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# outputs/out/reasoning-70b-stratos
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This model is a fine-tuned version of [meta-llama/Llama-3.1-70B](https://huggingface.co/meta-llama/Llama-3.1-70B) on the bespokelabs/Bespoke-Stratos-17k dataset.
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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: 5e-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: 16
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- total_train_batch_size: 64
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- total_eval_batch_size: 64
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: rex
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- lr_scheduler_warmup_steps: 20
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- num_epochs: 3.0
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### Training results
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### Framework versions
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- Transformers 4.49.0
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- Pytorch 2.5.1+cu124
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- Datasets 3.2.0
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- Tokenizers 0.21.0
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axolotl/stratos.yaml
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base_model: meta-llama/Llama-3.1-70B
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# Automatically upload checkpoint and final model to HF
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# hub_model_id: username/custom_model_name
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#
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plugins:
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- axolotl.integrations.liger.LigerPlugin
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8 |
+
- axolotl.integrations.spectrum.SpectrumPlugin
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+
spectrum_top_fraction: 0.5
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+
spectrum_model_name: meta-llama/Meta-Llama-3.1-70B
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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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+
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strict: false
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+
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chat_template: llama3
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+
datasets:
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- path: bespokelabs/Bespoke-Stratos-17k
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+
field_messages: conversations
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+
message_property_mappings:
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+
content: value
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+
role: from
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+
split: train
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+
type: chat_template
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+
dataset_prepared_path: last_run_prepared
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+
val_set_size: 0.0
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output_dir: ./outputs/out/reasoning-70b-stratos
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save_safetensors: true
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wandb_project: reasoning-70b-stratos
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wandb_entity: axolotl-ai
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wandb_watch:
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wandb_name:
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wandb_log_model:
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+
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+
sequence_len: 16384
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+
sample_packing: true
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+
pad_to_sequence_len: true
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+
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gradient_accumulation_steps: 1
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micro_batch_size: 4
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num_epochs: 3
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optimizer: adamw_torch_fused
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lr_scheduler: rex
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learning_rate: 5.0e-6
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embedding_lr: 1.0e-5
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max_grad_norm: 1.0
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train_on_inputs: false
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group_by_length: false
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bf16: true
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tf32: true
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gradient_checkpointing: offload
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gradient_checkpointing_kwargs:
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use_reentrant: true
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logging_steps: 1
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flash_attention: true
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warmup_steps: 20
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evals_per_epoch: 4
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saves_per_epoch: 1
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weight_decay: 0.01
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deepspeed: deepspeed_configs/zero3_bf16_cpuoffload_params.json
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special_tokens:
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pad_token: <|finetune_right_pad_id|>
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eos_token: <|eot_id|>
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added_tokens_overrides:
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128011: <think>
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128012: </think>
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128013: <|begin_of_thought|>
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128014: <|end_of_thought|>
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128015: <|begin_of_solution|>
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128016: <|end_of_solution|>
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fix_untrained_tokens:
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- 128011
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+
- 128012
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+
- 128013
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- 128014
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- 128015
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- 128016
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