---
library_name: transformers
base_model: minpeter/tiny-ko-124m-base-muon
tags:
- axolotl
- generated_from_trainer
datasets:
- HuggingFaceTB/smol-smoltalk
- trillionlabs/multisystem-curated
- allenai/tulu-3-sft-personas-instruction-following
- lemon-mint/smol-koreantalk
- lemon-mint/Korean-FineTome-100k
- heegyu/open-korean-instructions-v20231020
- coastral/korean-writing-style-instruct
- devngho/korean-instruction-mix
model-index:
- name: tiny-ko-124m-sft-muon
results: []
---
[
](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config
axolotl version: `0.12.0.dev0`
```yaml
base_model: minpeter/tiny-ko-124m-base-muon
hub_model_id: minpeter/tiny-ko-124m-sft-muon
output_dir: ./outputs/tiny-ko-124m-sft-muon
wandb_project: "axolotl"
wandb_entity: "kasfiekfs-e"
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
strict: false
chat_template: chatml
datasets:
- path: HuggingFaceTB/smol-smoltalk
type: chat_template
split: train
field_messages: messages
message_property_mappings:
role: role
content: content
- path: trillionlabs/multisystem-curated
type: chat_template
split: train
field_messages: messages
message_property_mappings:
role: role
content: content
- path: allenai/tulu-3-sft-personas-instruction-following
type: chat_template
split: train
field_messages: messages
message_property_mappings:
role: role
content: content
- path: lemon-mint/smol-koreantalk
type: chat_template
split: train
field_messages: messages
message_property_mappings:
role: role
content: content
- path: lemon-mint/Korean-FineTome-100k
type: chat_template
split: train
field_messages: messages
message_property_mappings:
role: role
content: content
- path: heegyu/open-korean-instructions-v20231020
type: chat_template
split: train
field_messages: conversations
message_property_mappings:
role: from
content: value
roles:
user: ["human", "user"]
assistant: ["gpt", "assistant", "bot"]
system: ["system", "input"]
- path: coastral/korean-writing-style-instruct
type: chat_template
split: train
field_messages: conversations
message_property_mappings:
role: from
content: value
- path: devngho/korean-instruction-mix
type: chat_template
split: train
field_messages: messages
message_property_mappings:
role: from
content: value
dataset_prepared_path: last_run_prepared
val_set_size: 0.001
save_safetensors: true
sequence_len: 2048
sample_packing: false
pad_to_sequence_len: false
use_pose: true
pose_max_context_len: 65536
overrides_of_model_config:
rope_theta: 10000.0
max_position_embeddings: 65536
gradient_accumulation_steps: 8
micro_batch_size: 32
num_epochs: 1
optimizer: muon
lr_scheduler: cosine
learning_rate: 3e-4
train_on_inputs: false
group_by_length: false
bf16: true
fp16:
tf32: true
gradient_checkpointing: false
gradient_checkpointing_kwargs:
use_reentrant: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
sdp_attention:
s2_attention:
save_steps: 200
warmup_steps: 20
eval_steps: 200
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
```
# tiny-ko-124m-sft-muon
This model is a fine-tuned version of [minpeter/tiny-ko-124m-base-muon](https://huggingface.co/minpeter/tiny-ko-124m-base-muon) on the HuggingFaceTB/smol-smoltalk, the trillionlabs/multisystem-curated, the allenai/tulu-3-sft-personas-instruction-following, the lemon-mint/smol-koreantalk, the lemon-mint/Korean-FineTome-100k, the heegyu/open-korean-instructions-v20231020, the coastral/korean-writing-style-instruct and the devngho/korean-instruction-mix datasets.
It achieves the following results on the evaluation set:
- Loss: 1.6461
## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 1024
- total_eval_batch_size: 128
- 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: 20
- training_steps: 6865
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0 | 0 | 2.4581 |
| 1.8892 | 0.1165 | 200 | 1.9059 |
| 1.802 | 0.2331 | 400 | 1.8333 |
| 1.7906 | 0.3496 | 600 | 1.7918 |
| 1.7761 | 0.4661 | 800 | 1.7638 |
| 1.7145 | 0.5827 | 1000 | 1.7423 |
| 1.7114 | 0.6992 | 1200 | 1.7255 |
| 1.6798 | 0.8157 | 1400 | 1.7123 |
| 1.6722 | 0.9323 | 1600 | 1.7006 |
| 1.6821 | 1.0484 | 1800 | 1.6928 |
| 1.6414 | 1.1649 | 2000 | 1.6864 |
| 1.6473 | 1.2814 | 2200 | 1.6794 |
| 1.6202 | 1.3980 | 2400 | 1.6729 |
| 1.6141 | 1.5145 | 2600 | 1.6689 |
| 1.6415 | 1.6310 | 2800 | 1.6645 |
| 1.6165 | 1.7476 | 3000 | 1.6603 |
| 1.6292 | 1.8641 | 3200 | 1.6573 |
| 1.6277 | 1.9806 | 3400 | 1.6541 |
| 1.6033 | 2.0967 | 3600 | 1.6537 |
| 1.6432 | 2.2133 | 3800 | 1.6517 |
| 1.602 | 2.3298 | 4000 | 1.6505 |
| 1.6435 | 2.4463 | 4200 | 1.6493 |
| 1.5941 | 2.5629 | 4400 | 1.6481 |
| 1.594 | 2.6794 | 4600 | 1.6473 |
| 1.5986 | 2.7959 | 4800 | 1.6468 |
| 1.586 | 2.9125 | 5000 | 1.6464 |
| 1.6146 | 3.0286 | 5200 | 1.6462 |
| 1.5985 | 3.1451 | 5400 | 1.6462 |
| 1.574 | 3.2616 | 5600 | 1.6462 |
| 1.5823 | 3.3782 | 5800 | 1.6460 |
| 1.597 | 3.4947 | 6000 | 1.6460 |
| 1.5859 | 3.6112 | 6200 | 1.6460 |
| 1.5769 | 3.7277 | 6400 | 1.6459 |
| 1.572 | 3.8443 | 6600 | 1.6459 |
| 1.6111 | 3.9608 | 6800 | 1.6461 |
### Framework versions
- Transformers 4.53.1
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1