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---
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library_name: peft
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license: apache-2.0
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base_model: Qwen/Qwen2.5-1.5B-Instruct
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tags:
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- axolotl
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- generated_from_trainer
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language:
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- zho
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- eng
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- fra
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- spa
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- por
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- deu
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- ita
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- rus
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- jpn
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- kor
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- vie
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- tha
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- ara
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model-index:
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- name: a7706e92-133c-4e5d-bca1-aad5a4fc27e6
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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: `0.4.1`
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```yaml
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accelerate_config:
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dynamo_backend: inductor
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mixed_precision: bf16
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num_machines: 1
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num_processes: auto
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use_cpu: false
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adapter: lora
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base_model: Qwen/Qwen2.5-1.5B-Instruct
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bf16: auto
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chat_template: llama3
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dataset_prepared_path: null
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datasets:
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- data_files:
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- 733c43e45c9d282a_train_data.json
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ds_type: json
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format: custom
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path: /workspace/input_data/733c43e45c9d282a_train_data.json
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type:
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field_instruction: problem
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field_output: solution
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format: '{instruction}'
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no_input_format: '{instruction}'
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system_format: '{system}'
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system_prompt: ''
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debug: null
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deepspeed: null
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device_map: auto
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early_stopping_patience: null
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eval_max_new_tokens: 128
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eval_table_size: null
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evals_per_epoch: 4
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flash_attention: false
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fp16: null
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fsdp: null
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fsdp_config: null
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gradient_accumulation_steps: 16
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gradient_checkpointing: true
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group_by_length: false
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hub_model_id: VERSIL91/a7706e92-133c-4e5d-bca1-aad5a4fc27e6
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hub_repo: null
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hub_strategy: checkpoint
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hub_token: null
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learning_rate: 0.0001
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local_rank: null
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logging_steps: 1
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lora_alpha: 16
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lora_dropout: 0.05
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lora_fan_in_fan_out: null
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lora_model_dir: null
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lora_r: 8
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lora_target_linear: true
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lora_target_modules:
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- q_proj
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- v_proj
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lr_scheduler: cosine
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max_memory:
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0: 70GiB
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max_steps: 50
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micro_batch_size: 2
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mlflow_experiment_name: /tmp/733c43e45c9d282a_train_data.json
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model_type: AutoModelForCausalLM
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num_epochs: 1
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optimizer: adamw_bnb_8bit
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output_dir: miner_id_24
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pad_to_sequence_len: true
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quantization_config:
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llm_int8_enable_fp32_cpu_offload: true
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load_in_8bit: true
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resume_from_checkpoint: null
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s2_attention: null
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sample_packing: false
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saves_per_epoch: 4
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sequence_len: 512
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strict: false
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tf32: false
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tokenizer_type: AutoTokenizer
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torch_compile: true
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train_on_inputs: false
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trust_remote_code: true
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val_set_size: 0.05
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wandb_entity: null
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wandb_mode: online
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wandb_name: a7706e92-133c-4e5d-bca1-aad5a4fc27e6
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wandb_project: Gradients-On-Demand
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wandb_run: your_name
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wandb_runid: a7706e92-133c-4e5d-bca1-aad5a4fc27e6
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warmup_steps: 10
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weight_decay: 0.0
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xformers_attention: null
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```
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</details><br>
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# a7706e92-133c-4e5d-bca1-aad5a4fc27e6
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This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5173
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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: 0.0001
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- train_batch_size: 2
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- eval_batch_size: 2
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- seed: 42
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- gradient_accumulation_steps: 16
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- total_train_batch_size: 32
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- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 10
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- training_steps: 50
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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| 0.6358 | 0.0001 | 1 | 0.6944 |
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| 0.5682 | 0.0019 | 13 | 0.6041 |
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| 0.5173 | 0.0039 | 26 | 0.5379 |
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| 0.5272 | 0.0058 | 39 | 0.5173 |
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### Framework versions
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- PEFT 0.13.2
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- Transformers 4.46.0
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- Pytorch 2.5.0+cu124
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- Datasets 3.0.1
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- Tokenizers 0.20.1 |
