File size: 5,502 Bytes

---
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: []
license: apache-2.0
language:
- ja
---

## Qwen2.5-1.75B-A1.1B-Instruct-ja

Qwen2.5-0.5B系のモデルを組み合わせて作ったMoEです。

## Details

https://zenn.dev/kendama/articles/68ae234e9371ac

<!-- 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