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Physics-Synthetic-Data/
Qwen2.5-coder-7B-caption2TikZ-SFT

Name: Qwen2.5-coder-7B-caption2TikZ-SFT
Availability: InStock

generated_from_trainer

conversational

text-generation-inference

endpoints_compatible

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See axolotl config

axolotl version: 0.10.0

yaml

# ---------------- 核心模型路径 ----------------
base_model: /nfs/data/johnsonk/mllm_models/RAFT/Oct2/lr1e-5_epoch4_warm0.03_training_6k_GPU_0_1_2_3_20251001_172635/trained_model   
# output_dir: /work/nvme/bckr/wli18/RAFT/exp_1
dataset_prepared_path: last_run_prepared
# --------------- 数据集 ----------------------
datasets:
  # 训练集
  - path: /home/johnsonk/RAFT/1exp/exp_1.0.2_questionize_high_school_physics_861_img/result/image_captions_2_sft_data_format_shuffled.jsonl
    type: chat_template
    split: train
    field_messages: messages
    message_property_mappings:
      role: role
      content: content
val_set_size: 0.05    

# --------------- 训练超参 ---------------------

sequence_len: 2048
micro_batch_size: 1 
gradient_accumulation_steps: 8
num_epochs: 3 
learning_rate: 1e-5
optimizer: adamw_torch
lr_scheduler: cosine
warmup_ratio: 0.03
weight_decay: 0.01



# max_steps: 100  # <--- 新增此行，强制运行100步
# --------------- 精度 / 显存 ------------------
bf16: auto
tf32: true
gradient_checkpointing: true
# gradient_checkpointing_kwargs:
#   use_reentrant: false

# --------------- DeepSpeed --------------------
deepspeed: /home/johnsonk/RAFT/configs/deepspeed_stage3.json

# ----------- 关闭所有 LoRA / 量化 -------------
# (配置已为全参数训练)
# load_in_4bit: true
# adapter: qlora
# lora_r: 16
# lora_alpha: 32
# lora_target_modules:
#   - q_proj
#   - k_proj
#   - v_proj
#   - o_proj
#   - down_proj
#   - up_proj
# lora_mlp_kernel: true
# lora_qkv_kernel: true
# lora_o_kernel: true

# -------------- 其它杂项 ----------------------
strict: false
chat_template: qwen_25
save_steps: 100000  # 每50个step保存一次

# [关键] 设置最多只保留2个最新的checkpoint，自动删除旧的
save_total_limit: 0

evals_per_epoch: 1
# saves_per_epoch: 100
logging_steps: 1
flash_attention: true


### WandB ###
report_to: wandb
wandb_project: test_axolotl
wandb_entity: johnson0213-ucla
wandb_name: Qwen2.5-Coder-7B-TikZ-SFT-860-v1-3epoch-grad_acc8-lr1e-5

nfs/data/johnsonk/mllm_models/RAFT/Nov15sftPhase/Nov15_sftPhase_lr1e-5_ep3_20251115_051615/trained_model

This model was trained from scratch on the /home/johnsonk/RAFT/1exp/exp_1.0.2_questionize_high_school_physics_861_img/result/image_captions_2_sft_data_format_shuffled.jsonl dataset. It achieves the following results on the evaluation set:

Loss: 0.4049

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: 1e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 8
total_train_batch_size: 32
total_eval_batch_size: 4
optimizer: Use OptimizerNames.ADAMW_TORCH 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: 2
training_steps: 67

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0	0	1.0554
0.4585	1.0	23	0.4498
0.4266	2.0	46	0.4049

Framework versions

Transformers 4.52.3
Pytorch 2.5.1
Datasets 3.6.0
Tokenizers 0.21.2

Physics-Synthetic-Data/Qwen2.5-coder-7B-caption2TikZ-SFT

nfs/data/johnsonk/mllm_models/RAFT/Nov15sftPhase/Nov15_sftPhase_lr1e-5_ep3_20251115_051615/trained_model

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Physics-Synthetic-Data/
Qwen2.5-coder-7B-caption2TikZ-SFT