agent-0924-incremental

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<details><summary>See axolotl config</summary>

axolotl version: 0.12.2

# 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: /workspace/merged_model
# 数据集设置
chat_template: qwen3
datasets:
  - path: /workspace/train_dir_0925/incremental_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: checkpoints
sequence_len: 8192 # 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: 16 # lora_r默认首选 16，平衡精度与显存
lora_alpha: 32 # 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: 2 # 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: 1
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 2e-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

flash_attention: true
warmup_ratio: 0.03
evals_per_epoch: 1   # 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>

checkpoints

This model was trained from scratch on the /workspace/train_dir_0925/incremental_data.json dataset.

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: 2e-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• 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
• training_steps: 18

Training results

Framework versions

• PEFT 0.17.0
• Transformers 4.55.2
• Pytorch 2.6.0+cu126
• Datasets 4.0.0
• Tokenizers 0.21.4