Instructions to use properly59/Jumini-Ko-1.2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use properly59/Jumini-Ko-1.2B with Transformers:

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

pipe = pipeline("text-generation", model="properly59/Jumini-Ko-1.2B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("properly59/Jumini-Ko-1.2B")
model = AutoModelForCausalLM.from_pretrained("properly59/Jumini-Ko-1.2B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use properly59/Jumini-Ko-1.2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "properly59/Jumini-Ko-1.2B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "properly59/Jumini-Ko-1.2B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/properly59/Jumini-Ko-1.2B

SGLang

How to use properly59/Jumini-Ko-1.2B 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 "properly59/Jumini-Ko-1.2B" \
    --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": "properly59/Jumini-Ko-1.2B",
		"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 "properly59/Jumini-Ko-1.2B" \
        --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": "properly59/Jumini-Ko-1.2B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use properly59/Jumini-Ko-1.2B with Docker Model Runner:
```
docker model run hf.co/properly59/Jumini-Ko-1.2B
```

properly59 commited on Jun 2

Commit

0157946

verified ·

1 Parent(s): a586bc6

Clarify Apache-2.0 model license and data provenance

Browse files

Files changed (2) hide show

LICENSE +17 -0
README.md +15 -1

LICENSE ADDED Viewed

	@@ -0,0 +1,17 @@

+Apache License
+Version 2.0, January 2004
+https://www.apache.org/licenses/
+Copyright 2026 Jumini-Ko HW2 contributors
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+    https://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.

README.md CHANGED Viewed

@@ -1,7 +1,7 @@
 ---
 language:
 - ko
-license: other
 library_name: transformers
 pipeline_tag: text-generation
 tags:
@@ -34,6 +34,20 @@ The current export candidate is the V3A retention step-6200 checkpoint. A later
 Additional post-export probes were also rejected. A V3B TinyStories low-ratio probe improved its own validation split from step 6200 to step 6300, but it did not beat step 6200 on a same-condition source-bucket comparison and generation remained repetitive/malformed. The exported artifact therefore remains the V3A step-6200 base checkpoint.
 ## Local Validation
 Train-time held-out validation on the V3A retention split improved monotonically through step 6200:

 ---
 language:
 - ko
+license: apache-2.0
 library_name: transformers
 pipeline_tag: text-generation
 tags:
 Additional post-export probes were also rejected. A V3B TinyStories low-ratio probe improved its own validation split from step 6200 to step 6300, but it did not beat step 6200 on a same-condition source-bucket comparison and generation remained repetitive/malformed. The exported artifact therefore remains the V3A step-6200 base checkpoint.
+## License and Data Provenance
+The released model artifacts are provided under Apache-2.0. The final promoted training sources were selected from datasets with explicit permissive licenses:
+| Source | Role | License |
+| --- | --- | --- |
+| `lcw99/wikipedia-korean-20240501` | Korean wiki pretraining/replay | Apache-2.0 |
+| `eliceai/korean-webtext-edu` | Korean educational web pretraining/replay | MIT |
+| `maywell/korean_textbooks` | textbook/factual synthetic Korean data | Apache-2.0 |
+| `heegyu/open-korean-instructions` | capped phase-2 instruction/chat anneal | MIT |
+| `IkJun1/korean-qa-dataset` | capped phase-2 Korean QA anneal | MIT |
+Benchmark and diagnostic datasets were kept evaluation-only and were not promoted into the final training mixture.
 ## Local Validation
 Train-time held-out validation on the V3A retention split improved monotonically through step 6200: