File size: 5,299 Bytes
b3bd8da |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 |
---
library_name: transformers
base_model: Kendamarron/Qwen2.5-4x0.5B-cpt
tags:
- axolotl
- generated_from_trainer
datasets:
- Kendamarron/jimba-instruction-all
- Kendamarron/OpenMathInstruct-2-ja-CoT-only_thought
- Aratako/Synthetic-JP-EN-Coding-Dataset-801k
- llm-jp/magpie-sft-v1.0
model-index:
- name: Qwen2.5-4x0.5B-sft-v1
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: `0.6.0`
```yaml
# 学習のベースモデルに関する設定
base_model: Kendamarron/Qwen2.5-4x0.5B-cpt
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
# 学習後のモデルのHFへのアップロードに関する設定
hub_model_id: Kendamarron/Qwen2.5-4x0.5B-sft-v1
hub_strategy: "end"
push_dataset_to_hub:
hf_use_auth_token: true
# Liger Kernelの設定（学習の軽量・高速化）
plugins:
- axolotl.integrations.liger.LigerPlugin
liger_cross_entropy: false
liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: true
# 量子化に関する設定
load_in_8bit: false
load_in_4bit: false
# SFTに利用するchat templateの設定
chat_template: qwen_25
# 学習データセットの前処理に関する設定
datasets:
- path: Kendamarron/jimba-instruction-all
split: train
type: chat_template
field_messages: conversations
message_field_role: role
message_field_content: content
- path: Kendamarron/OpenMathInstruct-2-ja-CoT-only_thought
split: train
type: chat_template
field_messages: messages
message_field_role: role
message_field_content: content
- path: Aratako/Synthetic-JP-EN-Coding-Dataset-801k
split: train[0:10000]
type: chat_template
field_messages: messages
message_field_role: role
message_field_content: content
- path: llm-jp/magpie-sft-v1.0
split: train[0:30000]
type: chat_template
field_messages: conversations
message_field_role: role
message_field_content: content
# データセット、モデルの出力先に関する設定
shuffle_merged_datasets: true
dataset_prepared_path: /workspace/data/sft-data
output_dir: /workspace/data/models/Qwen2.5-4x0.5B-SFT
# valid datasetのサイズ
val_set_size: 0.005
# wandbに関する設定
wandb_project: Qwen2.5-4x0.5B
wandb_entity: kendamarron
wandb_watch:
wandb_name: sft-v1
wandb_log_model:
# 学習に関する様々な設定
sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true
gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
cosine_min_lr_ratio: 0.1
learning_rate: 2e-5
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: false
early_stopping_patience:
auto_resume_from_checkpoints: true
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 1
warmup_steps: 60
eval_steps: 100
eval_batch_size: 1
eval_table_size:
eval_max_new_tokens:
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:
eos_token: "<|im_end|>"
pad_token: "<|end_of_text|>"
tokens:
- "<|im_start|>"
- "<|im_end|>"
```
</details><br>
# Qwen2.5-4x0.5B-sft-v1
This model is a fine-tuned version of [Kendamarron/Qwen2.5-4x0.5B-cpt](https://huggingface.co/Kendamarron/Qwen2.5-4x0.5B-cpt) on the Kendamarron/jimba-instruction-all, the Kendamarron/OpenMathInstruct-2-ja-CoT-only_thought, the Aratako/Synthetic-JP-EN-Coding-Dataset-801k and the llm-jp/magpie-sft-v1.0 datasets.
It achieves the following results on the evaluation set:
- Loss: 1.0085
## 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
- 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_BNB 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: 60
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.3068 | 0.0033 | 1 | 1.3071 |
| 1.1087 | 0.3309 | 100 | 1.0806 |
| 1.1393 | 0.6617 | 200 | 1.0488 |
| 1.0569 | 0.9926 | 300 | 1.0286 |
| 0.9902 | 1.3209 | 400 | 1.0215 |
| 0.9933 | 1.6518 | 500 | 1.0133 |
| 0.9706 | 1.9826 | 600 | 1.0085 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0
|