cjkasbdkjnlakb
/

agent-0924

+---
+library_name: peft
+license: apache-2.0
+base_model: Qwen/Qwen3-4B-Instruct-2507
+tags:
+- axolotl
+- base_model:adapter:Qwen/Qwen3-4B-Instruct-2507
+- lora
+- transformers
+datasets:
+- custom
+pipeline_tag: text-generation
+model-index:
+- name: workspace/train_dir_0924/checkpoints
+  results: []
+---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+[<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)
+<details><summary>See axolotl config</summary>
+axolotl version: `0.12.2`
+```yaml
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+# 是否以 8-bit 精度加载模型
+load_in_8bit: false
+# 是否以 4-bit 精度加载模型（与QLoRA绑定, 强制使用）
+load_in_4bit: false
+# 是否严格匹配模型结构，关闭表示可加载少部分差异结构（如以适配 adapter）
+# strict: false
+base_model: Qwen/Qwen3-4B-Instruct-2507
+# 数据集设置
+chat_template: qwen3
+datasets:
+  - path: /workspace/train_dir_0924/all_data.json # - 表示列表（list）中的一项, 即可以同时使用多个数据集
+    type: chat_template # chat_template(自定义格式) alpaca
+    roles_to_train: ["assistant"]
+    field_messages: messages # 标识的字段
+    message_property_mappings:  # message_property_mappings={'role':'role', 'content':'content'})
+      role: role
+      content: content
+dataset_prepared_path:
+val_set_size: 0.08
+output_dir: /workspace/train_dir_0924/checkpoints
+sequence_len: 16384 # 模型所能处理的最大上下文长度(默认2048)
+pad_to_sequence_len: false
+# context_parallel_size: 2 # 长序列拆分至多个GPU(强制要求 mirco_batch_size: 1)
+sample_packing: false # 在训练时将多个样本拼接（packing）成一个长序列(sequence_len)输入到模型中，以提高训练效率。
+eval_sample_packing: false # 评估时拼接多个样本
+# 训练超参数
+adapter: lora  # lora qlora
+lora_model_dir:
+lora_r: 32 # lora_r默认首选 16，平衡精度与显存
+lora_alpha: 64 # 缩放系数，用于控制 LoRA 的影响力, 一般设为 2*r 或 4*r
+lora_dropout: 0.05    # 从0.05改为0.1，增加dropout
+lora_target_linear: true
+micro_batch_size: 4 # 微批次大小 94G的H100可以设为4(Token为1w)
+gradient_accumulation_steps: 4 # 梯度累积: 将多个微批次的梯度(micro_batch_size)累积起来，然后更新模型权重 有效 Batch 常取 16: 小于 8 训练会抖，大于 32 只会更耗时、收益有限
+auto_find_batch_size: false # 允许Axolotl不断调整batch_size  ⚠️Zero-3不适用
+num_epochs: 3
+optimizer: adamw_torch_fused
+lr_scheduler: cosine
+learning_rate: 5e-5
+# bf16: auto + tf32: true，可获得更好的稳定性和性能。
+bf16: auto
+tf32: true
+# early_stopping_patience:
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+# auto_resume_from_checkpoints: true #自动从output_dir寻找最新checkpoint断点恢复
+logging_steps: 1
+logging_dir: /workspace/train_dir_0924/logs
+flash_attention: true
+warmup_ratio: 0.03
+evals_per_epoch: 8   # 增加评估频次，从4改为8
+saves_per_epoch: 1   # 增加保存频次，便于选择最佳checkpoint
+weight_decay: 0.01   # 从0.0改为0.01，增加正则化
+fsdp:
+  - full_shard
+  - auto_wrap
+fsdp_config:
+  fsdp_limit_all_gathers: true
+  fsdp_sync_module_states: true
+  fsdp_offload_params: false  # H200显存足够，无需offload
+  fsdp_use_orig_params: false
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: Qwen3DecoderLayer
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_sharding_strategy: FULL_SHARD
+```
+</details><br>
+# workspace/train_dir_0924/checkpoints
+This model is a fine-tuned version of [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) on the /workspace/train_dir_0924/all_data.json dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0463
+- Memory/max Mem Active(gib): 95.98
+- Memory/max Mem Allocated(gib): 95.98
+- Memory/device Mem Reserved(gib): 111.41
+## Model description
+More information needed
+## Intended uses & limitations
+More information needed
+## Training and evaluation data
+More information needed
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 4
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 64
+- total_eval_batch_size: 16
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 47
+- training_steps: 1578
+### Training results
+| Training Loss | Epoch  | Step | Validation Loss | Mem Active(gib) | Mem Allocated(gib) | Mem Reserved(gib) |
+|:-------------:|:------:|:----:|:---------------:|:---------------:|:------------------:|:-----------------:|
+| No log        | 0      | 0    | 0.9792          | 48.13           | 47.95              | 48.76             |
+| 0.0796        | 0.1255 | 66   | 0.0796          | 62.91           | 62.71              | 73.19             |
+| 0.0615        | 0.2511 | 132  | 0.0662          | 66.5            | 66.31              | 76.92             |
+| 0.064         | 0.3766 | 198  | 0.0616          | 66.5            | 66.31              | 76.92             |
+| 0.0673        | 0.5021 | 264  | 0.0591          | 72.68           | 72.48              | 84.29             |
+| 0.0574        | 0.6277 | 330  | 0.0568          | 72.68           | 72.48              | 84.29             |
+| 0.053         | 0.7532 | 396  | 0.0553          | 72.68           | 72.48              | 84.29             |
+| 0.0631        | 0.8787 | 462  | 0.0543          | 72.68           | 72.48              | 84.29             |
+| 0.0571        | 1.0038 | 528  | 0.0528          | 72.68           | 72.48              | 84.29             |
+| 0.0507        | 1.1293 | 594  | 0.0522          | 72.8            | 72.6               | 84.29             |
+| 0.0498        | 1.2549 | 660  | 0.0513          | 72.8            | 72.6               | 84.29             |
+| 0.0446        | 1.3804 | 726  | 0.0507          | 72.8            | 72.6               | 84.29             |
+| 0.0525        | 1.5059 | 792  | 0.0498          | 95.98           | 95.98              | 111.41            |
+| 0.0518        | 1.6315 | 858  | 0.0493          | 95.98           | 95.98              | 111.41            |
+| 0.0445        | 1.7570 | 924  | 0.0486          | 95.98           | 95.98              | 111.41            |
+| 0.0593        | 1.8825 | 990  | 0.0482          | 95.98           | 95.98              | 111.41            |
+| 0.0491        | 2.0076 | 1056 | 0.0478          | 95.98           | 95.98              | 111.41            |
+| 0.0459        | 2.1331 | 1122 | 0.0475          | 95.98           | 95.98              | 111.41            |
+| 0.0393        | 2.2587 | 1188 | 0.0471          | 95.98           | 95.98              | 111.41            |
+| 0.048         | 2.3842 | 1254 | 0.0468          | 95.98           | 95.98              | 111.41            |
+| 0.0532        | 2.5097 | 1320 | 0.0466          | 95.98           | 95.98              | 111.41            |
+| 0.0395        | 2.6353 | 1386 | 0.0465          | 95.98           | 95.98              | 111.41            |
+| 0.0457        | 2.7608 | 1452 | 0.0464          | 95.98           | 95.98              | 111.41            |
+| 0.0528        | 2.8864 | 1518 | 0.0463          | 95.98           | 95.98              | 111.41            |
+### Framework versions
+- PEFT 0.17.0
+- Transformers 4.55.2
+- Pytorch 2.6.0+cu126
+- Datasets 4.0.0
+- Tokenizers 0.21.4