Instructions to use oz1115/jy_test_mode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use oz1115/jy_test_mode with Transformers:

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

pipe = pipeline("text-generation", model="oz1115/jy_test_mode")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("oz1115/jy_test_mode")
model = AutoModelForCausalLM.from_pretrained("oz1115/jy_test_mode")
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

vLLM

How to use oz1115/jy_test_mode with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "oz1115/jy_test_mode"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "oz1115/jy_test_mode",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/oz1115/jy_test_mode

SGLang

How to use oz1115/jy_test_mode 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 "oz1115/jy_test_mode" \
    --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": "oz1115/jy_test_mode",
		"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 "oz1115/jy_test_mode" \
        --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": "oz1115/jy_test_mode",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use oz1115/jy_test_mode with Docker Model Runner:
```
docker model run hf.co/oz1115/jy_test_mode
```

We are thrilled to introduce Alpha-Instruct, our latest language model, which demonstrates exceptional capabilities in both Korean and English. Alpha-Instruct is developed using the Evolutionary Model Merging technique, enabling it to excel in complex language tasks and logical reasoning.

A key aspect of Alpha-Instruct's development is our community-based approach. We draw inspiration and ideas from various communities, shaping our datasets, methodologies, and the model itself. In return, we are committed to sharing our insights with the community, providing detailed information on the data, methods, and models used in Alpha-Instruct's creation.

Alpha-Instruct has achieved outstanding performance on the LogicKor, scoring an impressive 6.62. Remarkably, this performance rivals that of 70B models, showcasing the efficiency and power of our 8B model. This achievement highlights Alpha-Instruct's advanced computational and reasoning skills, making it a leading choice for diverse and demanding language tasks.

For more information and technical details about Alpha-Instruct, stay tuned to our updates and visit our website (Soon).

Overview

Alpha-Instruct is our latest language model, developed using 'Evolutionary Model Merging' technique. This method employs a 1:1 ratio of task-specific datasets from KoBEST and Haerae, resulting in a model with named 'Alpha-Ko-8B-Evo'. The following models were used for merging:

Meta-Llama-3-8B (Base)
Meta-Llama-3-8B-Instruct (Instruct)
Llama-3-Open-Ko-8B (Continual Pretrained)

To refine and enhance Alpha-Instruct, we utilized a carefully curated high-quality datasets aimed at 'healing' the model's output, significantly boosting its human preference scores. We use ORPO specifically for this "healing" (RLHF) phase. The datasets* used include:

*Some of these datasets were partially used and translated for training, and we ensured there was no contamination during the evaluation process.

This approach effectively balances human preferences with the model's capabilities, making Alpha-Instruct well-suited for real-life scenarios where user satisfaction and performance are equally important.

Benchmark Results

Results in LogicKor* are as follows:

Model	Single turn*	Multi turn*	Overall*
MLP-KTLim/llama-3-Korean-Bllossom-8B	4.238	3.404	3.821
Alpha-Ko-Evo	5.143	5.238	5.190
Alpha-Ko-Instruct (alt)	7.095	6.571	6.833
Alpha-Ko-Instruct	7.143	6.065	6.620
Alpha-Ko-Instruct-marlin (4bit)	6.857	5.738	6.298

*Self report(Default settings with 'alpha' template, mean of 3).

Result in KoBEST(acc, num_shot=5) are as follows:

Task	beomi/Llama-3-Open-Ko-8B-Instruct	maywell/Llama-3-Ko-8B-Instruct	Alpha-Ko-Evo	Alpha-Ko-Instruct
kobest overall	0.6220	0.6852	0.7229	0.7055
kobest_boolq	0.6254	0.7208	0.8547	0.8369
kobest_copa	0.7110	0.7650	0.7420	0.7420
kobest_hellaswag	0.3840	0.4440	0.4220	0.4240
kobest_sentineg	0.8388	0.9194	0.9471	0.9244
kobest_wic	0.5738	0.6040	0.6095	0.5730

*For reference, 'merged' models are chosen.

How to use

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "allganize/Llama-3-Alpha-Ko-Instruct"

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

messages = [
    {"role": "system", "content": "당신은 인공지능 어시스턴트입니다. 묻는 말에 친절하고 정확하게 답변하세요."},
    {"role": "user", "content": "피보나치 수열이 뭐야? 그리고 피보나치 수열에 대해 파이썬 코드를 짜줘볼래?"},
]

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|>")
]

outputs = model.generate(
    input_ids,
    max_new_tokens=512,
    eos_token_id=terminators,
    do_sample=False,
    repetition_penalty=1.05,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))

Correspondence to

Ji soo Kim (jisoo.kim@allganize.ai)
Contributors
- Sangmin Jeon (sangmin.jeon@allganize.ai)
- Seungwoo Ryu (seungwoo.ryu@allganize.ai)

Special Thanks

@beomi for providing us with a great model!

License

The use of this model is governed by the META LLAMA 3 COMMUNITY LICENSE AGREEMENT

Citation

If you use this model in your research, please cite it as follows:

@misc{alpha-instruct,
  author       = {Ji soo Kim},
  title        = {Alpha-Instruct: Allganize Bilingual Model},
  year         = {2024},
  publisher = {Hugging Face},
  journal = {Hugging Face repository},
  url          = {https://huggingface.co/allganize/Llama-3-Alpha-Ko-8B-Instruct},
}

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Paper for oz1115/jy_test_mode

ORPO: Monolithic Preference Optimization without Reference Model

Paper • 2403.07691 • Published Mar 12, 2024 • 72