Modelpublic

cjkasbdkjnlakb/agent-1011

sourceHugging Faceapache-2.0updated 1y agoView on Hugging Face

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Model Card

<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/> <details><summary>See axolotl config</summary>

axolotl version: 0.12.2

yaml

# 基础模型配置
base_model: Qwen/Qwen3-4B-Instruct-2507
load_in_8bit: false
load_in_4bit: false  # QLoRA才需要4bit

# LoRA 适配器配置 - 这是关键部分
adapter: lora  # 明确指定使用LoRA
lora_model_dir:  # 如果有预训练的LoRA权重可以在这里指定

# LoRA 具体参数
lora_r: 64
lora_alpha: 128
lora_dropout: 0.05
lora_target_modules:  # Qwen3模型的关键模块
  - q_proj
  - k_proj
  - v_proj
  - o_proj
  - gate_proj
  - up_proj
  - down_proj
lora_target_linear: true  # 自动找到所有线性层
lora_fan_in_fan_out: false

# 数据集设置
chat_template: qwen3
datasets:
  - path: /workspace/workspace/tool_data.json
    type: chat_template
    roles_to_train: ["assistant"]
    field_messages: messages
    message_property_mappings:
      role: role
      content: content

dataset_prepared_path:
val_set_size: 0.05
output_dir: /workspace/checkpoints

# 序列长度设置
sequence_len: 10000
pad_to_sequence_len: true
sample_packing: false
eval_sample_packing: false
group_by_length: true  # 启用长度分组，提高效率

# 训练超参数
num_epochs: 3
micro_batch_size: 4  # H100显存大
gradient_accumulation_steps: 4  # 8卡LoRA不需要太大的累积
eval_batch_size: 8

# 优化器设置
optimizer: adamw_torch_fused
lr_scheduler: cosine_with_restarts
cosine_restarts: 2  # 每个epoch重启一次
learning_rate: 4e-5
warmup_ratio: 0.03
weight_decay: 0.05

# 精度设置
bf16: auto  # H100支持bf16
tf32: true
gradient_checkpointing: true  # 节省显存
flash_attention: true

# 日志和保存
logging_steps: 30
evals_per_epoch: 1
saves_per_epoch: 1
save_total_limit: 3  # 只保留最新的3个checkpoint

# 多卡训练配置 - 使用DeepSpeed而不是FSDP
deepspeed: /workspace/workspace/zero2.json # 或者直接内联配置

# 其他优化
ddp_timeout: 3600  # DDP超时设置
ddp_find_unused_parameters: false  # LoRA通常不需要

</details><br>

workspace/checkpoints

This model is a fine-tuned version of Qwen/Qwen3-4B-Instruct-2507 on the /workspace/workspace/tool_data.json dataset. It achieves the following results on the evaluation set:

—Loss: 0.0482
—Memory/max Mem Active(gib): 123.28
—Memory/max Mem Allocated(gib): 123.28
—Memory/device Mem Reserved(gib): 124.72

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: 4e-05
—trainbatchsize: 4
—evalbatchsize: 8
—seed: 42
—distributed_type: multi-GPU
—num_devices: 8
—gradientaccumulationsteps: 4
—totaltrainbatch_size: 128
—totalevalbatch_size: 64
—optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
—lrschedulertype: cosinewithrestarts
—lrschedulerwarmup_steps: 39
—training_steps: 1316

Training results

Training Loss	Epoch	Step	Validation Loss	Mem Active(gib)	Mem Allocated(gib)	Mem Reserved(gib)
No log	0	0	1.1193	123.25	123.25	124.05
0.0509	1.0	439	0.0503	123.28	123.28	124.72
0.0461	2.0	878	0.0482	123.28	123.28	124.72

Framework versions

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