Instructions to use spow12/POLAR-14B_4.3_very_big_sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use spow12/POLAR-14B_4.3_very_big_sft with Transformers:

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

pipe = pipeline("text-generation", model="spow12/POLAR-14B_4.3_very_big_sft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("spow12/POLAR-14B_4.3_very_big_sft")
model = AutoModelForCausalLM.from_pretrained("spow12/POLAR-14B_4.3_very_big_sft", 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 spow12/POLAR-14B_4.3_very_big_sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "spow12/POLAR-14B_4.3_very_big_sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "spow12/POLAR-14B_4.3_very_big_sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/spow12/POLAR-14B_4.3_very_big_sft

SGLang

How to use spow12/POLAR-14B_4.3_very_big_sft 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 "spow12/POLAR-14B_4.3_very_big_sft" \
    --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": "spow12/POLAR-14B_4.3_very_big_sft",
		"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 "spow12/POLAR-14B_4.3_very_big_sft" \
        --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": "spow12/POLAR-14B_4.3_very_big_sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use spow12/POLAR-14B_4.3_very_big_sft with Docker Model Runner:
```
docker model run hf.co/spow12/POLAR-14B_4.3_very_big_sft
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

spow12/POLAR-14B_4.3_very_big_sft

Model Description

This model is a Supervised fine-tuned version of x2bee/POLAR-14B-v0.2 with DeepSpeed and trl for korean.

Trained Data

Trained with public data and private data and Generated data (about 50k)

Usage

from transformers import TextStreamer, pipeline, AutoTokenizer, AutoModelForCausalLM

model_id = 'spow12/POLAR-14B_4.3_very_big_sft'
tokenizer = AutoTokenizer.from_pretrained(model_id)
# %%
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2", 
    device_map='auto',
)
model.eval()

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device_map='auto')

streamer = TextStreamer(tokenizer)

generation_configs = dict(
    max_new_tokens=2048,
    num_return_sequences=1, 
    temperature=0.1,
    # early_stopping=True,
    repetition_penalty=1.2,
    num_beams=1,
    do_sample=True,
    top_k=20,
    top_p=0.9,
    eos_token_id=tokenizer.eos_token_id,
    pad_token_id=tokenizer.eos_token_id,
    streamer=streamer
)

sys_message = """당신은 친절한 챗봇으로서 상대방의 요청에 최대한 자세하고 친절하게 답해야합니다. 
사용자가 제공하는 정보를 세심하게 분석하여 사용자의 의도를 신속하게 파악하고 그에 따라 답변을 생성해야합니다.  

항상 매우 자연스러운 한국어로 응답하세요."""

message = [
    {
        'role': "system",
        'content': sys_message
    },
    {
        'role': 'user',
        'content': "현재의 경제상황에 대해 어떻게 생각해?."
    }
]
conversation = pipe(message, **generation_configs)
conversation[-1]