Instructions to use minpeter/tiny-ko-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use minpeter/tiny-ko-base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="minpeter/tiny-ko-base")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("minpeter/tiny-ko-base")
model = AutoModelForCausalLM.from_pretrained("minpeter/tiny-ko-base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use minpeter/tiny-ko-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "minpeter/tiny-ko-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "minpeter/tiny-ko-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/minpeter/tiny-ko-base

SGLang

How to use minpeter/tiny-ko-base 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-base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "minpeter/tiny-ko-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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-base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "minpeter/tiny-ko-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use minpeter/tiny-ko-base with Docker Model Runner:
```
docker model run hf.co/minpeter/tiny-ko-base
```

minpeter commited on Jun 5, 2025

Commit

8b0e3cd

verified ·

1 Parent(s): bc2dcf6

Training in progress, step 1000

Browse files

Files changed (3) hide show

config.json +3 -2
model.safetensors +1 -1
training_args.bin +2 -2

config.json CHANGED Viewed

@@ -12,12 +12,13 @@
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   "intermediate_size": 1536,
   "is_llama_config": true,
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   "mlp_bias": false,
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   "num_hidden_layers": 30,
   "num_key_value_heads": 3,
   "pretraining_tp": 1,
   "rms_norm_eps": 1e-05,
   "rope_interleaved": false,
@@ -25,7 +26,7 @@
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   "tie_word_embeddings": true,
   "torch_dtype": "float32",
-  "transformers_version": "4.51.3",
   "use_cache": true,
   "vocab_size": 128256
 }

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   "rope_interleaved": false,
   "rope_theta": 100000,
   "tie_word_embeddings": true,
   "torch_dtype": "float32",
+  "transformers_version": "4.52.4",
   "use_cache": true,
   "vocab_size": 128256
 }

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