Instructions to use jmkim-KR1/scenario-ax31-light-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jmkim-KR1/scenario-ax31-light-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("skt/A.X-3.1-Light")
model = PeftModel.from_pretrained(base_model, "jmkim-KR1/scenario-ax31-light-lora")

Transformers

How to use jmkim-KR1/scenario-ax31-light-lora with Transformers:

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

pipe = pipeline("text-generation", model="jmkim-KR1/scenario-ax31-light-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("jmkim-KR1/scenario-ax31-light-lora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jmkim-KR1/scenario-ax31-light-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jmkim-KR1/scenario-ax31-light-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jmkim-KR1/scenario-ax31-light-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jmkim-KR1/scenario-ax31-light-lora

SGLang

How to use jmkim-KR1/scenario-ax31-light-lora 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 "jmkim-KR1/scenario-ax31-light-lora" \
    --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": "jmkim-KR1/scenario-ax31-light-lora",
		"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 "jmkim-KR1/scenario-ax31-light-lora" \
        --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": "jmkim-KR1/scenario-ax31-light-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use jmkim-KR1/scenario-ax31-light-lora with Docker Model Runner:
```
docker model run hf.co/jmkim-KR1/scenario-ax31-light-lora
```

scenario-ax31-light-lora / special_tokens_map.json

jmkim-KR1

Exp F: A.X-3.1-Light QLoRA adapter (v3 data, best eval_loss 0.966)

acbcf15 verified 8 months ago

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	{
	"additional_special_tokens": [
	"<\|endoftext\|>",
	"<\|pad\|>",
	"<\|unk\|>",
	"<\|sep\|>",
	"<\|mask\|>",
	"<\|cls\|>",
	"<\|image\|>",
	"<\|audio\|>",
	"<\|user\|>",
	"<\|system\|>",
	"<\|assistant\|>",
	"<\|extra_id_0\|>",
	"<\|extra_id_1\|>",
	"<\|extra_id_2\|>",
	"<\|extra_id_3\|>",
	"<\|extra_id_4\|>",
	"<\|extra_id_5\|>",
	"<\|extra_id_6\|>",
	"<\|extra_id_7\|>",
	"<\|extra_id_8\|>",
	"<\|extra_id_9\|>",
	"<\|extra_id_10\|>",
	"<\|extra_id_13\|>",
	"<\|im_start\|>",
	"<\|im_sep\|>",
	"<\|im_end\|>",
	"<\|resident_reg\|>",
	"<\|foreigner_reg\|>",
	"<\|business_reg\|>",
	"<\|credit_card\|>",
	"<\|passport\|>",
	"<\|driver_license\|>",
	"<\|telephone\|>",
	"<\|health_insurance\|>",
	"<\|bank_account\|>"
	],
	"bos_token": {
	"content": "<\|endoftext\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	},
	"cls_token": {
	"content": "<\|cls\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	},
	"eos_token": {
	"content": "<\|im_end\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	},
	"mask_token": {
	"content": "<\|mask\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	},
	"pad_token": {
	"content": "<\|pad\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	},
	"sep_token": {
	"content": "<\|sep\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	},
	"unk_token": {
	"content": "<\|unk\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	}
	}