Instructions to use ryanu/EEVE-10.8-BOOK-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ryanu/EEVE-10.8-BOOK-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="ryanu/EEVE-10.8-BOOK-v0.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ryanu/EEVE-10.8-BOOK-v0.1")
model = AutoModelForCausalLM.from_pretrained("ryanu/EEVE-10.8-BOOK-v0.1")
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 ryanu/EEVE-10.8-BOOK-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ryanu/EEVE-10.8-BOOK-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ryanu/EEVE-10.8-BOOK-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ryanu/EEVE-10.8-BOOK-v0.1

SGLang

How to use ryanu/EEVE-10.8-BOOK-v0.1 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 "ryanu/EEVE-10.8-BOOK-v0.1" \
    --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": "ryanu/EEVE-10.8-BOOK-v0.1",
		"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 "ryanu/EEVE-10.8-BOOK-v0.1" \
        --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": "ryanu/EEVE-10.8-BOOK-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ryanu/EEVE-10.8-BOOK-v0.1 with Docker Model Runner:
```
docker model run hf.co/ryanu/EEVE-10.8-BOOK-v0.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

파라미터	값
Task	Book (사회과학, 기술과학, 철학, 법학, 예술 등)
데이터 크기	5000개
모델	qlora
max_seq_length	1024
num_train_epochs	3
per_device_train_batch_size	8
gradient_accumulation_steps	32
evaluation_strategy	"steps"
eval_steps	2000
logging_steps	25
optim	"paged_adamw_8bit"
learning_rate	2e-4
lr_scheduler_type	"cosine"
warmup_steps	10
warmup_ratio	0.05
report_to	"tensorboard"
weight_decay	0.01
max_steps	-1

Summary

Book

모델 이름	Rouge-1	Rouge-2	Rouge-L
*ryanu/EEVE-10.8-BOOK-v0.1	0.2454	0.1158	0.2404
meta-llama/llama-3-70b-instruct	0.2269	0.0925	0.2186
meta-llama/llama-3-8b-instruct	0.2137	0.0883	0.2020
yanolja/EEVE-Korean-Instruct-2.8B-v1.0	0.2095	0.0866	0.1985
mistralai/mixtral-8x7b-instruct-v0-1	0.1735	0.0516	0.1668
ibm-mistralai/mixtral-8x7b-instruct-v01-q	0.1724	0.0534	0.1630

Paper

모델 이름	Rouge-1	Rouge-2	Rouge-L
*meta-llama/llama-3-8b-instruct	0.2044	0.0868	0.1895
ryanu/EEVE-10.8-BOOK-v0.1	0.2004	0.0860	0.1938
meta-llama/llama-3-70b-instruct	0.1935	0.0783	0.1836
yanolja/EEVE-Korean-Instruct-2.8B-v1.0	0.1934	0.0829	0.1832
mistralai/mixtral-8x7b-instruct-v0-1	0.1774	0.0601	0.1684
ibm-mistralai/mixtral-8x7b-instruct-v01-q	0.1702	0.0561	0.1605

RAG Q&A

모델 이름	Rouge-1	Rouge-2	Rouge-L
*meta-llama/llama-3-70b-instruct	0.4418	0.2986	0.4297
*meta-llama/llama-3-8b-instruct	0.4391	0.3100	0.4273
mistralai/mixtral-8x7b-instruct-v0-1	0.4022	0.2653	0.3916
ibm-mistralai/mixtral-8x7b-instruct-v01-q	0.3105	0.1763	0.2960
yanolja/EEVE-Korean-Instruct-10.8B-v1.0	0.3191	0.2069	0.3136
ryanu/EEVE-10.8-BOOK-v0.1	0.2185	0.1347	0.2139

prompt template

다음 문장을 3~5문장으로 반복되는 구문없이 텍스트에 제시된 주요 논거를 간략하게 요약해줘.

문장: {context}

요약: {summary}

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Safetensors

Model size

11B params

Tensor type

F16

Model tree for ryanu/EEVE-10.8-BOOK-v0.1

Quantizations

1 model