Text Generation
Transformers
Safetensors
llama
axolotl
Generated from Trainer
conversational
text-generation-inference
Instructions to use minpeter/tiny-ko-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use minpeter/tiny-ko-sft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="minpeter/tiny-ko-sft") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("minpeter/tiny-ko-sft") model = AutoModelForCausalLM.from_pretrained("minpeter/tiny-ko-sft", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use minpeter/tiny-ko-sft with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "minpeter/tiny-ko-sft" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "minpeter/tiny-ko-sft", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/minpeter/tiny-ko-sft
- SGLang
How to use minpeter/tiny-ko-sft with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "minpeter/tiny-ko-sft" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "minpeter/tiny-ko-sft", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "minpeter/tiny-ko-sft" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "minpeter/tiny-ko-sft", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use minpeter/tiny-ko-sft with Docker Model Runner:
docker model run hf.co/minpeter/tiny-ko-sft
| library_name: transformers | |
| base_model: minpeter/tiny-ko-base | |
| tags: | |
| - axolotl | |
| - generated_from_trainer | |
| datasets: | |
| - lemon-mint/Korean-FineTome-100k | |
| - lemon-mint/smol-koreantalk | |
| - heegyu/open-korean-instructions-v20231020 | |
| - FreedomIntelligence/evol-instruct-korean | |
| - FreedomIntelligence/alpaca-gpt4-korean | |
| - FreedomIntelligence/sharegpt-korean | |
| - coastral/korean-writing-style-instruct | |
| - devngho/korean-instruction-mix | |
| - youjunhyeok/Magpie-Pro-300K-Filtered-ko | |
| - youjunhyeok/smoltalk-ko-translate | |
| model-index: | |
| - name: tiny-ko-sft | |
| 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: `0.10.0.dev0` | |
| ```yaml | |
| base_model: minpeter/tiny-ko-base | |
| hub_model_id: minpeter/tiny-ko-sft | |
| output_dir: ./outputs/tiny-ko-sft | |
| wandb_project: "axolotl" | |
| wandb_entity: "kasfiekfs-e" | |
| chat_template: chatml | |
| datasets: | |
| - path: lemon-mint/Korean-FineTome-100k | |
| type: chat_template | |
| split: train[:10%] | |
| field_messages: messages | |
| message_property_mappings: | |
| role: role | |
| content: content | |
| - path: lemon-mint/smol-koreantalk | |
| type: chat_template | |
| split: train[:10%] | |
| field_messages: messages | |
| message_property_mappings: | |
| role: role | |
| content: content | |
| - path: heegyu/open-korean-instructions-v20231020 | |
| type: chat_template | |
| split: train[:10%] | |
| field_messages: conversations | |
| message_property_mappings: | |
| role: from | |
| content: value | |
| roles: | |
| user: ["human", "user"] | |
| assistant: ["gpt", "assistant", "bot"] | |
| system: ["system", "input"] | |
| # NOTE: https://github.com/FreedomIntelligence/MultilingualSIFT | |
| - path: FreedomIntelligence/evol-instruct-korean | |
| type: chat_template | |
| split: train[:10%] | |
| field_messages: conversations | |
| message_property_mappings: | |
| role: from | |
| content: value | |
| - path: FreedomIntelligence/alpaca-gpt4-korean | |
| type: chat_template | |
| split: train[:10%] | |
| field_messages: conversations | |
| message_property_mappings: | |
| role: from | |
| content: value | |
| - path: FreedomIntelligence/sharegpt-korean | |
| type: chat_template | |
| split: train[:10%] | |
| field_messages: conversations | |
| message_property_mappings: | |
| role: from | |
| content: value | |
| - path: coastral/korean-writing-style-instruct | |
| type: chat_template | |
| split: train[:10%] | |
| field_messages: conversations | |
| message_property_mappings: | |
| role: from | |
| content: value | |
| - path: devngho/korean-instruction-mix | |
| type: chat_template | |
| split: train[:10%] | |
| field_messages: messages | |
| message_property_mappings: | |
| role: from | |
| content: value | |
| - path: youjunhyeok/Magpie-Pro-300K-Filtered-ko | |
| type: chat_template | |
| split: train[:10%] | |
| field_messages: conversations | |
| message_property_mappings: | |
| role: from | |
| content: value | |
| - path: youjunhyeok/smoltalk-ko-translate | |
| type: chat_template | |
| name: merge_filtered | |
| field_messages: conversations | |
| message_property_mappings: | |
| role: role | |
| content: content | |
| dataset_prepared_path: last_run_prepared | |
| val_set_size: 0.05 | |
| save_steps: 200 | |
| warmup_steps: 20 | |
| eval_steps: 200 | |
| sequence_len: 4096 | |
| # <<<< experimental settings <<<< | |
| sample_packing: true | |
| train_on_inputs: false | |
| # >>>> experimental settings >>> | |
| pad_to_sequence_len: true | |
| gradient_accumulation_steps: 4 | |
| micro_batch_size: 32 | |
| optimizer: paged_adamw_8bit | |
| lr_scheduler: cosine | |
| learning_rate: 1e-3 | |
| bf16: auto | |
| tf32: false | |
| gradient_checkpointing: true | |
| gradient_checkpointing_kwargs: | |
| use_reentrant: false | |
| resume_from_checkpoint: | |
| logging_steps: 1 | |
| flash_attention: true | |
| num_epochs: 1 | |
| weight_decay: 0.0 | |
| ``` | |
| </details><br> | |
| # tiny-ko-sft | |
| This model is a fine-tuned version of [minpeter/tiny-ko-base](https://huggingface.co/minpeter/tiny-ko-base) on the lemon-mint/Korean-FineTome-100k, the lemon-mint/smol-koreantalk, the heegyu/open-korean-instructions-v20231020, the FreedomIntelligence/evol-instruct-korean, the FreedomIntelligence/alpaca-gpt4-korean, the FreedomIntelligence/sharegpt-korean, the coastral/korean-writing-style-instruct, the devngho/korean-instruction-mix, the youjunhyeok/Magpie-Pro-300K-Filtered-ko and the youjunhyeok/smoltalk-ko-translate datasets. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.5297 | |
| ## 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: 0.001 | |
| - train_batch_size: 32 | |
| - eval_batch_size: 32 | |
| - seed: 42 | |
| - distributed_type: multi-GPU | |
| - num_devices: 2 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 256 | |
| - total_eval_batch_size: 64 | |
| - optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT 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: 817 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:------:|:----:|:---------------:| | |
| | 2.3518 | 0.0012 | 1 | 2.3640 | | |
| | 1.6322 | 0.2446 | 200 | 1.6913 | | |
| | 1.5903 | 0.4891 | 400 | 1.6003 | | |
| | 1.5146 | 0.7337 | 600 | 1.5392 | | |
| | 1.5277 | 0.9783 | 800 | 1.5297 | | |
| ### Framework versions | |
| - Transformers 4.52.3 | |
| - Pytorch 2.6.0+cu124 | |
| - Datasets 3.6.0 | |
| - Tokenizers 0.21.1 | |