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
See axolotl config
axolotl version: 0.6.0
# 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|>"
Qwen2.5-4x0.5B-sft-v1
This model is a fine-tuned version of 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
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Model tree for Kendamarron/Qwen2.5-1.75B-A1.1B-Instruct-ja
Base model
Kendamarron/Qwen2.5-4x0.5B-cpt