Instructions to use mncai/agiin-13.6B-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mncai/agiin-13.6B-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="mncai/agiin-13.6B-v0.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mncai/agiin-13.6B-v0.1")
model = AutoModelForCausalLM.from_pretrained("mncai/agiin-13.6B-v0.1", 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 mncai/agiin-13.6B-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mncai/agiin-13.6B-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": "mncai/agiin-13.6B-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mncai/agiin-13.6B-v0.1

SGLang

How to use mncai/agiin-13.6B-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 "mncai/agiin-13.6B-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": "mncai/agiin-13.6B-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 "mncai/agiin-13.6B-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": "mncai/agiin-13.6B-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mncai/agiin-13.6B-v0.1 with Docker Model Runner:
```
docker model run hf.co/mncai/agiin-13.6B-v0.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for mncai/agiin-13.6B-v0.1

Introduction of MindsAndCompany

https://mnc.ai/

We create various AI models and develop solutions that can be applied to businesses. And as for generative AI, we are developing products like Code Assistant, TOD Chatbot, LLMOps, and are in the process of developing Enterprise AGI (Artificial General Intelligence).

Model Summary

This model was built based on the Mistral architecture. It was inspired by neural connection technology and rehabilitation therapy. I have created a new model architecture that does not require pretraining, and training the model is sufficient with just one H100 for 7 hours.

Data

Intel/orca_dpo_pairs (DPO)

Surgery and Training

stack mistral 62 layers and DPO.

How to Use

message = [
    {"role": "system", "content": "You are a helpful assistant chatbot."},
    {"role": "user", "content": "두 개의 구가 각각 지름이 1, 2일때 두 구의 부피는 몇배지? 설명도 같이 해줘."}
]
tokenizer = AutoTokenizer.from_pretrained(hf_model)
prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)

pipeline = transformers.pipeline(
    "text-generation",
    model=hf_model,
    tokenizer=tokenizer
)


sequences = pipeline(
    prompt,
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
    num_return_sequences=1,
    max_length=512,
)
print(sequences[0]['generated_text'])

Contact

If you have any questions, please raise an issue or contact us at dwmyoung@mnc.ai

Downloads last month: 92

Safetensors

Model size

14B params

Tensor type

F16

Model tree for mncai/agiin-13.6B-v0.1

Quantizations

4 models

mncai
/

agiin-13.6B-v0.1