Instructions to use Saxo/yunsung-llama-2-koen-13b-linkbricks-sft-basic-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Saxo/yunsung-llama-2-koen-13b-linkbricks-sft-basic-v1 with Transformers:

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

pipe = pipeline("text-generation", model="Saxo/yunsung-llama-2-koen-13b-linkbricks-sft-basic-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Saxo/yunsung-llama-2-koen-13b-linkbricks-sft-basic-v1")
model = AutoModelForCausalLM.from_pretrained("Saxo/yunsung-llama-2-koen-13b-linkbricks-sft-basic-v1", 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 Saxo/yunsung-llama-2-koen-13b-linkbricks-sft-basic-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Saxo/yunsung-llama-2-koen-13b-linkbricks-sft-basic-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Saxo/yunsung-llama-2-koen-13b-linkbricks-sft-basic-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Saxo/yunsung-llama-2-koen-13b-linkbricks-sft-basic-v1

SGLang

How to use Saxo/yunsung-llama-2-koen-13b-linkbricks-sft-basic-v1 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 "Saxo/yunsung-llama-2-koen-13b-linkbricks-sft-basic-v1" \
    --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": "Saxo/yunsung-llama-2-koen-13b-linkbricks-sft-basic-v1",
		"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 "Saxo/yunsung-llama-2-koen-13b-linkbricks-sft-basic-v1" \
        --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": "Saxo/yunsung-llama-2-koen-13b-linkbricks-sft-basic-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Saxo/yunsung-llama-2-koen-13b-linkbricks-sft-basic-v1 with Docker Model Runner:
```
docker model run hf.co/Saxo/yunsung-llama-2-koen-13b-linkbricks-sft-basic-v1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for Model ID

AI 와 빅데이터 분석 전문 기업인 Linkbricks의 데이터사이언티스트인 지윤성 박사(Saxo)가 beomi/llama-2-koen-13b 베이스모델을 GCP상의 A100-40G 4개를 통해 SFT 훈련을 한(2048 Tokens) 인스트럭션 모델. Accelerate, Deepspeed Zero-3 라이브러리를 사용했으며 Flash Attention 은 Disable 로 설정

Dr. Yunsung Ji (Saxo), a data scientist at Linkbricks, a company specializing in AI and big data analytics, trained the beomi/llama-2-koen-13b base model on 4 A100-40Gs on GCP for 4 hours of instructional training (2048 Tokens). Accelerate, Deepspeed Zero-3 libraries were used.

www.linkbricks.com, www.linkbricks.vc

Configuration including BitsandBytes

learning_rate = 2e-4 num_epochs = 5 batch_size = 4 block_size = 2048 trainer = "sft" warmup_ratio = 0.1 weight_decay = 0.01 gradient_accumulation = 4 mixed_precision = "fp16" peft = True quantization = "int4" lora_r = 64 lora_alpha = 16 lora_dropout = 0.1 model_max_length = 2048

Dataset Format

Alpaca Format Prompt Text

Downloads last month: 5

Safetensors

Model size

13B params

Tensor type

BF16

Model tree for Saxo/yunsung-llama-2-koen-13b-linkbricks-sft-basic-v1

Quantizations

1 model

Saxo
/

yunsung-llama-2-koen-13b-linkbricks-sft-basic-v1

Model Card for Model ID

Configuration including BitsandBytes

learning_rate = 2e-4 num_epochs = 5 batch_size = 4 block_size = 2048 trainer = "sft" warmup_ratio = 0.1 weight_decay = 0.01 gradient_accumulation = 4 mixed_precision = "fp16" peft = True quantization = "int4" lora_r = 64 lora_alpha = 16 lora_dropout = 0.1 model_max_length = 2048

Dataset Format

Model tree for Saxo/yunsung-llama-2-koen-13b-linkbricks-sft-basic-v1

Datasets used to train Saxo/yunsung-llama-2-koen-13b-linkbricks-sft-basic-v1