122 lines
2.9 KiB
Markdown
122 lines
2.9 KiB
Markdown
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---
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library_name: peft
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license: gpl-3.0
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base_model: Orion-zhen/Meissa-Qwen2.5-7B-Instruct
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tags:
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- axolotl
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- base_model:adapter:Orion-zhen/Meissa-Qwen2.5-7B-Instruct
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- lora
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- transformers
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datasets:
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- chanceQZhang/zhihuhighvotes
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pipeline_tag: text-generation
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model-index:
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- name: outputs/zhihu-tech-career-lora
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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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# config_sft_zhihu.yml
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base_model: Orion-zhen/Meissa-Qwen2.5-7B-Instruct
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model_type: AutoModelForCausalLM
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tokenizer_type: AutoTokenizer
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# 使用您上传的数据集
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datasets:
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- path: chanceQZhang/zhihuhighvotes
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type: chat_template # ChatML 格式使用 chat_template
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split: train
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# 提速核心
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sample_packing: true
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pad_to_sequence_len: true
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# LoRA 配置
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adapter: lora
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lora_r: 8
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lora_alpha: 32
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lora_dropout: 0.1
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lora_target_modules:
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- q_proj
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- v_proj
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- k_proj
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- o_proj
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- gate_proj
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- up_proj
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- down_proj
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# --- 核心优化：显存节省配置 ---
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bf16: true # 30/40系列或A系列显卡必开，提升速度且省显存
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fp16: false
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gradient_checkpointing: true # 必开！用计算时间换空间，大幅降低显存占用
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flash_attention: true # 必开！大幅降低长文本下的显存需求
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# 训练配置
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sequence_len: 2048
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micro_batch_size: 6
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gradient_accumulation_steps: 3
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num_epochs: 2
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learning_rate: 0.00005
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# 减少中间开销
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logging_steps: 10
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eval_steps: 100
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save_steps: 302
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# 输出
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output_dir: ./outputs/zhihu-tech-career-lora
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```
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</details><br>
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# outputs/zhihu-tech-career-lora
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This model is a fine-tuned version of [Orion-zhen/Meissa-Qwen2.5-7B-Instruct](https://huggingface.co/Orion-zhen/Meissa-Qwen2.5-7B-Instruct) on the chanceQZhang/zhihuhighvotes 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-05
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- train_batch_size: 6
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- eval_batch_size: 6
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- seed: 42
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- gradient_accumulation_steps: 3
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- total_train_batch_size: 18
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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: 16
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- training_steps: 536
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### Training results
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### Framework versions
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- PEFT 0.18.1
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- Transformers 4.57.1
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- Pytorch 2.8.0+cu128
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- Datasets 4.4.2
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- Tokenizers 0.22.2
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