Instructions to use youngseok12/AX-3.1-Light-sft_v0_21_source_screen_numerical_300 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use youngseok12/AX-3.1-Light-sft_v0_21_source_screen_numerical_300 with Transformers:

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

pipe = pipeline("text-generation", model="youngseok12/AX-3.1-Light-sft_v0_21_source_screen_numerical_300")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("youngseok12/AX-3.1-Light-sft_v0_21_source_screen_numerical_300")
model = AutoModelForCausalLM.from_pretrained("youngseok12/AX-3.1-Light-sft_v0_21_source_screen_numerical_300", 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 youngseok12/AX-3.1-Light-sft_v0_21_source_screen_numerical_300 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "youngseok12/AX-3.1-Light-sft_v0_21_source_screen_numerical_300"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "youngseok12/AX-3.1-Light-sft_v0_21_source_screen_numerical_300",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/youngseok12/AX-3.1-Light-sft_v0_21_source_screen_numerical_300

SGLang

How to use youngseok12/AX-3.1-Light-sft_v0_21_source_screen_numerical_300 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 "youngseok12/AX-3.1-Light-sft_v0_21_source_screen_numerical_300" \
    --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": "youngseok12/AX-3.1-Light-sft_v0_21_source_screen_numerical_300",
		"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 "youngseok12/AX-3.1-Light-sft_v0_21_source_screen_numerical_300" \
        --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": "youngseok12/AX-3.1-Light-sft_v0_21_source_screen_numerical_300",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use youngseok12/AX-3.1-Light-sft_v0_21_source_screen_numerical_300 with Docker Model Runner:
```
docker model run hf.co/youngseok12/AX-3.1-Light-sft_v0_21_source_screen_numerical_300
```

AX-3.1-Light-sft_v0_21_source_screen_numerical_300

This is a standalone BF16 model obtained by fine-tuning skt/A.X-3.1-Light with a LoRA adapter and merging the adapter into the base weights. It is intended for Korean-language research and controlled evaluation. The repository contains no benchmark data, benchmark answers, training logs, or access credentials.

Model Details

Base model: skt/A.X-3.1-Light
Base revision used for training and merge: 9b41bb2406472634d8812c0b8931fa40fa9a6c3a
Architecture: unchanged from the base model
Weight format: BF16 safetensors
Chat template: official A.X tokenizer chat template
Custom Python model code: none
Submission form: merged full model; no separate adapter is required
Experiment condition: A v0.21-equivalent source-screening arm replacing 300 rows with numerical machine-reading examples.

Training Data

Each arm contains 5,801 training rows in a v0.21-equivalent source-screening mixture: 5,501 unchanged occurrences and 300 replacement occurrences. The replacement source for this model is AIHub-71568, 숫자연산 기계독해 데이터. The 300 examples were selected deterministically with seed 20260829; no quality, ranking, embedding, or model scoring was used for selection.

Public evaluation benchmarks such as KMMLU-Pro, CLIcK, HLE, SNU Ko-MuSR, Com2-main, and Original MuSR were not used as SFT data. The applicable terms of the AI Hub source data remain in force.

Training Procedure

Objective: assistant-only causal-language-model cross entropy
Epochs: 1
Learning rate: 5e-5
Optimizer: adamw_torch_fused
Scheduler: linear, no warmup
Weight decay: 0.0
Maximum gradient norm: 1.0
LoRA: rank 16, alpha 32, dropout 0.05
LoRA target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Per-device batch size: 1
Gradient accumulation: 8 (effective batch size 8)
Maximum sequence length: 2048
Precision: BF16
Packing: disabled
Random seed: 42

Local Evaluation

The full local canonical-suite evaluation used free and B1_constrained probes, 21,962 rows per model, and had zero generation errors. The following are the primary B1_constrained parsed-accuracy results; they are local evaluation results, not official K-AI leaderboard scores.

KMMLU-Pro 40.54%, CLIcK 64.31%, HLE(Ko) 4.31%, SNU Ko-MuSR 55.07%, Com2-main(Ko) 52.40%; five-axis mean 43.33%.

Usage

Load the repository with transformers AutoModelForCausalLM and AutoTokenizer, or directly with standard vLLM. The merged repository does not require a separate adapter or trust_remote_code.

Intended Use and Limitations

This model is an experimental Korean SFT model for research and controlled evaluation. It can produce factual errors and should not be used as a substitute for professional legal, accounting, medical, or financial advice.

License

The base model is distributed under the Apache License 2.0. The applicable terms of the AI Hub source data remain in force for use of the training data. See LICENSE for the base model license text.

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Safetensors

Model size

7B params

Tensor type

BF16

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