Instructions to use jwg0830/AX-3.1-Light-sft_v3_1b_0.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jwg0830/AX-3.1-Light-sft_v3_1b_0.5 with Transformers:

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

pipe = pipeline("text-generation", model="jwg0830/AX-3.1-Light-sft_v3_1b_0.5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

tokenizer = AutoTokenizer.from_pretrained("jwg0830/AX-3.1-Light-sft_v3_1b_0.5")
model = AutoModelForCausalLM.from_pretrained("jwg0830/AX-3.1-Light-sft_v3_1b_0.5", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jwg0830/AX-3.1-Light-sft_v3_1b_0.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jwg0830/AX-3.1-Light-sft_v3_1b_0.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jwg0830/AX-3.1-Light-sft_v3_1b_0.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jwg0830/AX-3.1-Light-sft_v3_1b_0.5

SGLang

How to use jwg0830/AX-3.1-Light-sft_v3_1b_0.5 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 "jwg0830/AX-3.1-Light-sft_v3_1b_0.5" \
    --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": "jwg0830/AX-3.1-Light-sft_v3_1b_0.5",
		"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 "jwg0830/AX-3.1-Light-sft_v3_1b_0.5" \
        --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": "jwg0830/AX-3.1-Light-sft_v3_1b_0.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jwg0830/AX-3.1-Light-sft_v3_1b_0.5 with Docker Model Runner:
```
docker model run hf.co/jwg0830/AX-3.1-Light-sft_v3_1b_0.5
```

jwg0830 commited on Aug 30

Commit

ed64140

verified ·

1 Parent(s): 0dfb983

rename to v3_1b_0.5 / K-AI version 3.15

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -12,7 +12,7 @@ tags:
 - k-ai-leaderboard
 ---
-# AX-3.1-Light-v0.21-x-v3.1b-safe-soup-0.5 (동전)
 `skt/A.X-3.1-Light` 기반 두 파인튜닝 모델 **sft_v0_21**과 **sft_v3_1b_safe**를 가중치
 평균(model soup, 각 0.5)해서 만든 모델입니다. 데이터 재학습이 아니라 두 모델의 전체

 - k-ai-leaderboard
 ---
+# AX-3.1-Light-sft_v3_1b_0.5 (동전, K-AI 등록 버전 3.15)
 `skt/A.X-3.1-Light` 기반 두 파인튜닝 모델 **sft_v0_21**과 **sft_v3_1b_safe**를 가중치
 평균(model soup, 각 0.5)해서 만든 모델입니다. 데이터 재학습이 아니라 두 모델의 전체