agent-0910

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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/xuyiwei/merged_agent_model_0908
# 数据集设置
chat_template: qwen3
datasets:
  - path: /workspace/jiangqiyi/file_manage_agent_train/data/retrieval_agent_train_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: 0.05
output_dir: models/0910
sequence_len: 8192 # 模型所能处理的最大上下文长度(默认2048)
pad_to_sequence_len: true
# 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: 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
lora_target_linear: true
micro_batch_size: 1 # 微批次大小 94G的H100可以设为4(Token为1w)
gradient_accumulation_steps: 16 # 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
# xformers_attention: true
warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
weight_decay: 0.0
# deepspeed: /workspace/deepspeed_configs/zero2.json
# fsdp:
#   - full_shard
#   - auto_wrap
# fsdp_config:
#   fsdp_limit_all_gathers: true
#   fsdp_sync_module_states: true
#   fsdp_offload_params: true
#   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
# special_tokens:
# wandb_project:
# wandb_entity:
# wandb_watch:
# wandb_name:
# wandb_log_model:

</details><br>

models/0910

This model was trained from scratch on the /workspace/jiangqiyi/file_manage_agent_train/data/retrieval_agent_train_data.json dataset. It achieves the following results on the evaluation set:

• Loss: 0.0452
• Memory/max Mem Active(gib): 23.87
• Memory/max Mem Allocated(gib): 23.87
• Memory/device Mem Reserved(gib): 26.56

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: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 16
• total_train_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: 10
• training_steps: 279

Training results

Framework versions

• PEFT 0.17.0
• Transformers 4.55.2
• Pytorch 2.6.0+cu124
• Datasets 4.0.0
• Tokenizers 0.21.4