180 lines
5.7 KiB
Markdown
180 lines
5.7 KiB
Markdown
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---
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library_name: transformers
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license: apache-2.0
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base_model: Qwen/Qwen3-8B
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tags:
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- generated_from_trainer
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datasets:
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- xiaolesu/OsmosisProofling-SFT
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model-index:
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- name: outputs/OsmosisProofling-SFT/
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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: `0.16.0.dev0`
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```yaml
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base_model: Qwen/Qwen3-8B
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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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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_layer_norm: true
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liger_fused_linear_cross_entropy: true
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chat_template: qwen3
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chat_template_kwargs:
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enable_thinking: false
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datasets:
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- path: xiaolesu/OsmosisProofling-SFT
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type: alpaca
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split: train
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test_datasets:
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- path: xiaolesu/OsmosisProofling-SFT
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type: alpaca
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split: validation
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output_dir: ./outputs/OsmosisProofling-SFT/
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sequence_len: 4096
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sample_packing: true
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flex_attention: true
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flex_attn_compile_kwargs:
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dynamic: false
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mode: max-autotune-no-cudagraphs
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wandb_project: OsmosisProofling-SFT
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wandb_entity:
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wandb_watch:
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wandb_name: OsmosisProofling-SFT-Run1
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wandb_log_model:
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gradient_accumulation_steps: 1
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micro_batch_size: 2
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num_epochs: 2
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optimizer: adamw_torch_fused
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lr_scheduler: cosine
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learning_rate: 1e-5
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bf16: true
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tf32: true
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resume_from_checkpoint:
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logging_steps: 5
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evals_per_epoch: 10
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saves_per_epoch: 10
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save_total_limit: 3
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warmup_ratio: 0.1
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weight_decay: 0.0
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fsdp:
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- full_shard
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- auto_wrap
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fsdp_config:
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fsdp_version: 2
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fsdp_offload_params: false
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fsdp_cpu_ram_efficient_loading: true
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fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
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fsdp_transformer_layer_cls_to_wrap: Qwen3DecoderLayer
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fsdp_state_dict_type: FULL_STATE_DICT
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fsdp_sharding_strategy: FULL_SHARD
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fsdp_reshard_after_forward: true
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fsdp_activation_checkpointing: true
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special_tokens:
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```
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</details><br>
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# outputs/OsmosisProofling-SFT/
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This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) on the xiaolesu/OsmosisProofling-SFT dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3543
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- Ppl: 1.4252
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- Memory/max Active (gib): 20.98
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- Memory/max Allocated (gib): 20.98
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- Memory/device Reserved (gib): 36.0
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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: 1e-05
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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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- distributed_type: multi-GPU
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- num_devices: 7
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- total_train_batch_size: 14
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- total_eval_batch_size: 14
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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: cosine
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- lr_scheduler_warmup_steps: 21
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- training_steps: 212
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Ppl | Active (gib) | Allocated (gib) | Reserved (gib) |
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|:-------------:|:------:|:----:|:---------------:|:------:|:------------:|:---------------:|:--------------:|
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| No log | 0 | 0 | 1.3417 | 3.8257 | 16.56 | 16.56 | 20.27 |
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| 1.2425 | 0.1048 | 11 | 0.9643 | 2.6231 | 20.98 | 20.98 | 36.1 |
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| 0.7372 | 0.2095 | 22 | 0.5572 | 1.7458 | 20.98 | 20.98 | 36.0 |
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| 0.5042 | 0.3143 | 33 | 0.4529 | 1.5728 | 20.98 | 20.98 | 36.0 |
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| 0.4350 | 0.4190 | 44 | 0.4158 | 1.5155 | 20.98 | 20.98 | 36.0 |
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| 0.3719 | 0.5238 | 55 | 0.3908 | 1.4782 | 20.98 | 20.98 | 36.0 |
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| 0.3934 | 0.6286 | 66 | 0.3780 | 1.4594 | 20.98 | 20.98 | 36.0 |
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| 0.3594 | 0.7333 | 77 | 0.3696 | 1.4471 | 20.98 | 20.98 | 36.0 |
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| 0.3513 | 0.8381 | 88 | 0.3645 | 1.4398 | 20.98 | 20.98 | 36.0 |
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| 0.3499 | 0.9429 | 99 | 0.3616 | 1.4356 | 20.98 | 20.98 | 36.0 |
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| 0.3517 | 1.0476 | 110 | 0.3583 | 1.4309 | 20.98 | 20.98 | 36.0 |
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| 0.3422 | 1.1524 | 121 | 0.3567 | 1.4286 | 20.98 | 20.98 | 36.0 |
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| 0.3219 | 1.2571 | 132 | 0.3557 | 1.4272 | 20.98 | 20.98 | 36.0 |
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| 0.3098 | 1.3619 | 143 | 0.3552 | 1.4264 | 20.98 | 20.98 | 36.0 |
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| 0.3068 | 1.4667 | 154 | 0.3546 | 1.4257 | 20.98 | 20.98 | 36.0 |
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| 0.3168 | 1.5714 | 165 | 0.3545 | 1.4254 | 20.98 | 20.98 | 36.0 |
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| 0.3198 | 1.6762 | 176 | 0.3546 | 1.4256 | 20.98 | 20.98 | 36.0 |
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| 0.3207 | 1.7810 | 187 | 0.3544 | 1.4253 | 20.98 | 20.98 | 36.0 |
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| 0.3232 | 1.8857 | 198 | 0.3541 | 1.4249 | 20.98 | 20.98 | 36.0 |
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| 0.3441 | 1.9905 | 209 | 0.3543 | 1.4252 | 20.98 | 20.98 | 36.0 |
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### Framework versions
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- Transformers 5.3.0
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- Pytorch 2.9.1+cu128
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- Datasets 4.5.0
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- Tokenizers 0.22.2
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