---
library_name: transformers
base_model: minpeter/tiny-ko-187m-base-250718
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-187m-sft-250718
results: []
---
[
](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config
axolotl version: `0.12.0.dev0`
```yaml
base_model: minpeter/tiny-ko-187m-base-250718
hub_model_id: minpeter/tiny-ko-187m-sft-250718
output_dir: ./outputs/tiny-ko-187m-sft-250718
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: 8192
sample_packing: false
pad_to_sequence_len: false
use_pose: true
pose_max_context_len: 65536
overrides_of_model_config:
rope_theta: 1000000.0
max_position_embeddings: 65536
gradient_accumulation_steps: 8
micro_batch_size: 16
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:
special_tokens:
eos_token: '<|im_end|>'
plugins:
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
- axolotl.integrations.liger.LigerPlugin
- axolotl.integrations.lm_eval.LMEvalPlugin
lm_eval_tasks:
- gsm8k
- hellaswag
- arc_easy
- arc_challenge
- piqa
- winogrande
- openbookqa
- wsc
- boolq
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
```
# tiny-ko-187m-sft-250718
This model is a fine-tuned version of [minpeter/tiny-ko-187m-base-250718](https://huggingface.co/minpeter/tiny-ko-187m-base-250718) 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: 1.6990
## 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: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- total_eval_batch_size: 64
- 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: 3470
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0 | 0 | 2.1799 |
| 1.8649 | 0.0576 | 200 | 1.8603 |
| 1.8031 | 0.1153 | 400 | 1.8033 |
| 1.7128 | 0.1729 | 600 | 1.7709 |
| 1.7758 | 0.2306 | 800 | 1.7492 |
| 1.7084 | 0.2882 | 1000 | 1.7339 |
| 1.7258 | 0.3458 | 1200 | 1.7225 |
| 1.6972 | 0.4035 | 1400 | 1.7149 |
| 1.73 | 0.4611 | 1600 | 1.7091 |
| 1.7166 | 0.5188 | 1800 | 1.7051 |
| 1.688 | 0.5764 | 2000 | 1.7025 |
| 1.737 | 0.6341 | 2200 | 1.7010 |
| 1.7322 | 0.6917 | 2400 | 1.6998 |
| 1.7133 | 0.7493 | 2600 | 1.6994 |
| 1.6953 | 0.8070 | 2800 | 1.6992 |
| 1.7233 | 0.8646 | 3000 | 1.6990 |
| 1.733 | 0.9223 | 3200 | 1.6990 |
| 1.7017 | 0.9799 | 3400 | 1.6990 |
### Framework versions
- Transformers 4.53.2
- Pytorch 2.7.1+cu126
- Datasets 4.0.0
- Tokenizers 0.21.2