Instructions to use teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf with Transformers:

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

pipe = pipeline("text-generation", model="teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf")
model = AutoModelForCausalLM.from_pretrained("teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf")
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]:]))

llama-cpp-python

How to use teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf",
	filename="EEVE-Korean-Instruct-10.8B-v1.0-FP16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf:Q4_K_M

Use Docker

docker model run hf.co/teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf:Q4_K_M

SGLang

How to use teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf 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 "teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf" \
    --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": "teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf",
		"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 "teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf" \
        --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": "teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf with Ollama:
```
ollama run hf.co/teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf:Q4_K_M
```

Unsloth Studio new

How to use teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf 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 teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf 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 teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf to start chatting

Docker Model Runner
How to use teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf with Docker Model Runner:
```
docker model run hf.co/teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf:Q4_K_M
```

Lemonade

How to use teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull teddylee777/EEVE-Korean-Instruct-10.8B-v1.0-gguf:Q4_K_M

Run and chat with the model

lemonade run user.EEVE-Korean-Instruct-10.8B-v1.0-gguf-Q4_K_M

List all available models

lemonade list

Original model is yanolja/EEVE-Korean-Instruct-10.8B-v1.0
quantized using llama.cpp

Ollama

Modelfile

FROM EEVE-Korean-Instruct-10.8B-v1.0-Q8_0.gguf

TEMPLATE """{{- if .System }}
<s>{{ .System }}</s>
{{- end }}
<s>Human:
{{ .Prompt }}</s>
<s>Assistant:
"""

SYSTEM """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions."""

PARAMETER temperature 0
PARAMETER num_predict 3000
PARAMETER num_ctx 4096
PARAMETER stop <s>
PARAMETER stop </s>

Training Data

Korean-translated version of Open-Orca/SlimOrca-Dedup
Korean-translated version of argilla/ultrafeedback-binarized-preferences-cleaned
No other dataset was used

Citation

@misc{kim2024efficient,
      title={Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models}, 
      author={Seungduk Kim and Seungtaek Choi and Myeongho Jeong},
      year={2024},
      eprint={2402.14714},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{cui2023ultrafeedback,
      title={UltraFeedback: Boosting Language Models with High-quality Feedback}, 
      author={Ganqu Cui and Lifan Yuan and Ning Ding and Guanming Yao and Wei Zhu and Yuan Ni and Guotong Xie and Zhiyuan Liu and Maosong Sun},
      year={2023},
      eprint={2310.01377},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{SlimOrcaDedup,
  title = {SlimOrca Dedup: A Deduplicated Subset of SlimOrca},
  author = {Wing Lian and Guan Wang and Bleys Goodson and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium" and Nathan Hoos},
  year = {2023},
  publisher = {HuggingFace},
  url = {https://huggingface.co/datasets/Open-Orca/SlimOrca-Dedup/}
}

@misc{mukherjee2023orca,
      title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, 
      author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
      year={2023},
      eprint={2306.02707},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}