133 lines
2.9 KiB
Markdown
133 lines
2.9 KiB
Markdown
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---
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library_name: transformers
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license: other
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base_model: LiquidAI/LFM2-350M-ENJP-MT
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tags:
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- generated_from_trainer
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datasets:
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- chotto-20251010.sft.jsonl
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model-index:
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- name: data/outputs/shisa-v2.1c-lfm2-350m-sft2
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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: `0.13.0.dev0`
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```yaml
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base_model: LiquidAI/LFM2-350M-ENJP-MT
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chunked_cross_entropy: true
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eot_tokens:
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- "<|im_end|>"
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datasets:
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- path: chotto-20251010.sft.jsonl
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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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- assistant
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- gpt
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- model
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user:
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- user
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- human
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roles_to_train: ["assistant"]
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dataset_prepared_path: last_run_prepared_sft
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output_dir: /data/outputs/shisa-v2.1c-lfm2-350m-sft2
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sequence_len: 8192
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sample_packing: true
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flash_attention: true
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pad_to_sequence_len: true
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neftune_noise_alpha: 5
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use_wandb: true
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wandb_entity: augmxnt
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wandb_project: liquid-hackathon-tokyo
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wandb_name: "shisa-v2.1c-lfm2-350m-sft2"
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# GBS = 128 / 8 GPU / 16 MBS / 1 GAS
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gradient_accumulation_steps: 1
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micro_batch_size: 16
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num_epochs: 4
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optimizer: adamw_torch_4bit
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lr_scheduler: cosine
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learning_rate: 6e-5 # 4.78 @ GBS=128
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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: 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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logging_steps: 1
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warmup_ratio: 0.03
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saves_per_epoch: 1
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deepspeed: zero3_bf16.json
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weight_decay: 1e-4
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```
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</details><br>
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# data/outputs/shisa-v2.1c-lfm2-350m-sft2
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This model is a fine-tuned version of [LiquidAI/LFM2-350M-ENJP-MT](https://huggingface.co/LiquidAI/LFM2-350M-ENJP-MT) on the chotto-20251010.sft.jsonl 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: 6e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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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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- total_train_batch_size: 128
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- total_eval_batch_size: 128
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- optimizer: Use OptimizerNames.ADAMW_TORCH_4BIT 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: 69
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- training_steps: 2332
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### Training results
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### Framework versions
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- Transformers 4.57.0
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- Pytorch 2.8.0+rocm6.4
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- Datasets 4.1.1
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- Tokenizers 0.22.1
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