Instructions to use Dongwookss/big_fut_final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Dongwookss/big_fut_final with Transformers:

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

pipe = pipeline("text-generation", model="Dongwookss/big_fut_final")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Dongwookss/big_fut_final")
model = AutoModelForCausalLM.from_pretrained("Dongwookss/big_fut_final")
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 Dongwookss/big_fut_final with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Dongwookss/big_fut_final

SGLang

How to use Dongwookss/big_fut_final 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 "Dongwookss/big_fut_final" \
    --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": "Dongwookss/big_fut_final",
		"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 "Dongwookss/big_fut_final" \
        --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": "Dongwookss/big_fut_final",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Dongwookss/big_fut_final 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 Dongwookss/big_fut_final 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 Dongwookss/big_fut_final to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Dongwookss/big_fut_final to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Dongwookss/big_fut_final",
    max_seq_length=2048,
)

Docker Model Runner
How to use Dongwookss/big_fut_final with Docker Model Runner:
```
docker model run hf.co/Dongwookss/big_fut_final
```

Model Name : 풋풋이(futfut)

Model Concept

풋살 도메인 친절한 도우미 챗봇을 구축하기 위해 LLM 파인튜닝과 RAG를 이용하였습니다.
Base Model : zephyr-7b-beta
풋풋이의 말투는 '해요'체를 사용하여 말끝에 '얼마든지 물어보세요~~! 풋풋~~!'로 종료합니다.

Serving by Fast API

Git repo : Dongwooks

Summary:

Unsloth 패키지를 사용하여 LoRA 진행하였습니다.
SFT Trainer를 통해 훈련을 진행
활용 데이터
- llm_futsaldata_yo
  - 말투 학습을 위해 '해요'체로 변환하고 인삿말을 넣어 모델 컨셉을 유지하였습니다.
Train for 7H 23M

Environment : Colab 환경에서 진행하였으며 L4 GPU를 사용하였습니다.

Model Load


#!pip install transformers==4.40.0 accelerate
import os
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = 'Dongwookss/big_fut_final'

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
model.eval()

Query

from transformers import TextStreamer
PROMPT = '''Below is an instruction that describes a task. Write a response that appropriately completes the request.
제시하는 context에서만 대답하고 context에 없는 내용은 모르겠다고 대답해'''

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

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

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

text_streamer = TextStreamer(tokenizer)
_ = model.generate(
    input_ids,
    max_new_tokens=4096,
    eos_token_id=terminators,
    do_sample=True,
    streamer = text_streamer,
    temperature=0.6,
    top_p=0.9,
    repetition_penalty = 1.1
)

Model Details

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

Developed by: Dongwookss
Model type: [More Information Needed]
Language(s) (NLP): Korean
Finetuned from model : HuggingFaceH4/zephyr-7b-beta

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Training Details

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Safetensors

Model size

7B params

Tensor type

BF16

Dataset used to train Dongwookss/big_fut_final

Space using Dongwookss/big_fut_final 1

Paper for Dongwookss/big_fut_final

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 50