Instructions to use UICHEOL-HWANG/EcomGen-Llama3.2-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use UICHEOL-HWANG/EcomGen-Llama3.2-3B with Transformers:

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

pipe = pipeline("text-generation", model="UICHEOL-HWANG/EcomGen-Llama3.2-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("UICHEOL-HWANG/EcomGen-Llama3.2-3B")
model = AutoModelForCausalLM.from_pretrained("UICHEOL-HWANG/EcomGen-Llama3.2-3B", 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 UICHEOL-HWANG/EcomGen-Llama3.2-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "UICHEOL-HWANG/EcomGen-Llama3.2-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "UICHEOL-HWANG/EcomGen-Llama3.2-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/UICHEOL-HWANG/EcomGen-Llama3.2-3B

SGLang

How to use UICHEOL-HWANG/EcomGen-Llama3.2-3B 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 "UICHEOL-HWANG/EcomGen-Llama3.2-3B" \
    --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": "UICHEOL-HWANG/EcomGen-Llama3.2-3B",
		"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 "UICHEOL-HWANG/EcomGen-Llama3.2-3B" \
        --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": "UICHEOL-HWANG/EcomGen-Llama3.2-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use UICHEOL-HWANG/EcomGen-Llama3.2-3B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for UICHEOL-HWANG/EcomGen-Llama3.2-3B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for UICHEOL-HWANG/EcomGen-Llama3.2-3B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for UICHEOL-HWANG/EcomGen-Llama3.2-3B to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="UICHEOL-HWANG/EcomGen-Llama3.2-3B",
    max_seq_length=2048,
)

Docker Model Runner
How to use UICHEOL-HWANG/EcomGen-Llama3.2-3B with Docker Model Runner:
```
docker model run hf.co/UICHEOL-HWANG/EcomGen-Llama3.2-3B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Uploaded finetuned model

Developed by: UICHEOL-HWANG
License: apache-2.0
Finetuned from model : Bllossom/llama-3.2-Korean-Bllossom-3B

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

위 모델은 Bllossom의 한국어 모델 Llama3.2-Korean-Bloosom-3B를 `Unsloth`를 통하여 훈련 시킨 모델입니다.

사용 방법

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch 

tokenizer = AutoTokenizer.from_pretrained("UICHEOL-HWANG/EcomGen-Llama3.2-3B")
model = AutoModelForCausalLM.from_pretrained(
    "UICHEOL-HWANG/EcomGen-Llama3.2-3B",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

instruction = """
상품명: 프리미엄 유기농 쌀 10kg
카테고리: 식품 > 쌀·잡곡
가격: 45,000원
핵심 키워드: 유기농, 쌀, 농부, 정성, 고가, 품질, 안전, 가족, 건강
작성 톤: 신뢰감_있는_전문가_톤 (품질 중심, 프리미엄 상품 강조)
"""

messages = [
    {"role": "user", "content": f"{instruction}"}
    ]

input_ids = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

terminators = [
    tokenizer.convert_tokens_to_ids("<|end_of_text|>"),
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

outputs = model.generate(
    input_ids,
    max_new_tokens=512,
    eos_token_id=terminators,
    do_sample=True,
    temperature=0.6,
    top_p=0.9
)

print(tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True))

파인튜닝 세부사항

데이터셋
- 원본 데이터: 약 9,000개의 상품 데이터
- 데이터 증강: GPT-4o-mini를 통한 상품 설명 생성 포맷으로 변환
- 최종 데이터셋: 약 23,000개
훈련 환경
- 훈련 시간: 약 4분 16초
- 컴퓨팅 자원: NVIDIA L4 (24GB VRAM)
- 훈련 프레임워크: Unsloth + Hugging Face TRL
- 베이스 모델: Llama-3.2-Korean-Bllossom-3B (3B 파라미터)
특화 분야
이 모델은 전자상거래 상품 설명 자동 생성에 최적화되어 있으며, 다양한 톤앤매너와 키워드 기반 상품 설명을 생성할 수 있습니다.

Downloads last month: 11

Safetensors

Model size

3B params

Tensor type

BF16

Model tree for UICHEOL-HWANG/EcomGen-Llama3.2-3B

Base model

Bllossom/llama-3.2-Korean-Bllossom-3B

Finetuned

(26)

this model

Quantizations

1 model

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