Instructions to use youngseok12/AX-3.1-Light-sft_source_screen_71568_3000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use youngseok12/AX-3.1-Light-sft_source_screen_71568_3000 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_source_screen_71568_3000")
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_source_screen_71568_3000")
model = AutoModelForCausalLM.from_pretrained("youngseok12/AX-3.1-Light-sft_source_screen_71568_3000", 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_source_screen_71568_3000 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_source_screen_71568_3000"
# 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_source_screen_71568_3000",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/youngseok12/AX-3.1-Light-sft_source_screen_71568_3000

SGLang

How to use youngseok12/AX-3.1-Light-sft_source_screen_71568_3000 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_source_screen_71568_3000" \
    --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_source_screen_71568_3000",
		"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_source_screen_71568_3000" \
        --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_source_screen_71568_3000",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use youngseok12/AX-3.1-Light-sft_source_screen_71568_3000 with Docker Model Runner:
```
docker model run hf.co/youngseok12/AX-3.1-Light-sft_source_screen_71568_3000
```

A.X-3.1-Light source screening — AI Hub 71568

This repository contains a standalone BF16 model obtained by fine-tuning skt/A.X-3.1-Light with LoRA SFT and merging the adapter into the pristine base model. It is an experimental Korean-language source-only screening arm for measuring the effect of the AI Hub 71568 economic and sports numerical machine-reading data. The model is directly loadable with Transformers or standard vLLM and does not require a separate adapter.

Base model

Hugging Face base model: skt/A.X-3.1-Light
Base revision: 9b41bb2406472634d8812c0b8931fa40fa9a6c3a
Architecture: LlamaForCausalLM (unchanged)
Weight format: BF16 safetensors
Submission form: merged full model

Training data

Only the AI Hub dataset 71568, 숫자연산 기계독해 데이터 was used for SFT. The training set contains exactly 3,000 TL examples: 1,500 경제 examples and 1,500 스포츠 examples. Five internal calculation/task strata were balanced at 300 examples each per category: 가산/감산, 비율연산, 양자/다자비교, 경계추출, and 단서추출. Each selected article contributes at most one example, and no exact duplicate, public benchmark row, v0.21 row, or other AI Hub source was included. Targets use the common answer-first form 정답: <값> without a generated rationale.

Training procedure

Objective: assistant-token causal-language-model cross entropy
Epochs: 1
Optimizer steps: 375
Learning rate: 5e-5
Scheduler: cosine, 3% warmup
Weight decay: 0.01
LoRA: rank 16, alpha 32, dropout 0.05, bias none
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 and data seed: 42
Total supervised target tokens: 45,002
Mean / median supervised target tokens: 15.0007 / 7
Truncation: 0 (overlength rows were excluded rather than truncated)
Final training loss: 0.2903463449
Internal AI Hub dev loss: 0.3073074222

Evaluation status

No public benchmark data was used for training, and no public benchmark score is claimed for this repository. The local post-merge smoke test loaded the merged model and generated 정답: 3 for a simple numeric question.

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "youngseok12/AX-3.1-Light-sft_source_screen_71568_3000"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16)

The model follows the base A.X chat template. Prompts used during SFT requested an answer-first response beginning with 정답: .

Intended use and limitations

This is an experimental Korean SFT model for research and controlled comparison. It can produce incorrect or unsupported answers and must not be used as a substitute for professional advice.

License

The base model and derived weights are distributed under the Apache License 2.0, subject to the original base-model terms. The applicable AI Hub dataset terms remain in force for the training data.

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Safetensors

Model size

7B params

Tensor type

BF16

Model tree for youngseok12/AX-3.1-Light-sft_source_screen_71568_3000

Base model

skt/A.X-3.1-Light

Finetuned

(18)

this model