---
license: mit
---

![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c0c845a04a514ba62bcd1a/RFpsPxlc_3cK0kmWj-tYR.png)

# **Introduction**  
We introduce Motif, a new language model family of [**Moreh**](https://moreh.io/), specialized in Korean and English.\
Motif-102B-Instruct is a chat model tuned from the base model [Motif-102B](https://huggingface.co/moreh/Motif-102B).

## Training Platform
- Motif-102B is trained on [**MoAI platform**](https://moreh.io/product), with AMD's MI250 GPU.
- The MoAI platform simplifies scalable, cost efficient training of large-scale models across multiple nodes.
- The MoAI platform also supports various optimized and automated parallelization without any complex manual works.
- One can find more information on the MoAI Platform in https://moreh.io/product
- Or, contact us directly [contact@moreh.io](mailto:contact@moreh.io)

## Quick Usage  
You can chat directly with our model Motif through our [Model hub](https://model-hub.moreh.io/).

## Details
More details will be provided in the upcoming technical report.

### Release Date  
2024.09.30

### Benchmark Results
    
| Model                        | KMMLU   |
|------------------------------|-------|
| GPT-4-base-0613\**| 57.62 |
| Llama3.1-70B-instruct *| 52.1 |
| **Motif-102B** \**+| 58.25 |
| Motif-102B-Instruct \**+| 57.98 |

‘*’ : Community reported  
‘**’ : Measured by the authors  
‘+’ : Indicates the model is specialized in Korean  


## How to use

### Use with vLLM
- Minimum requirements: 4xA100 80GB GPUs
- Refer to this [link](https://github.com/vllm-project/vllm) to install vllm  
```python
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams

# for minimum, we recommand using 4x A100 80GB GPUs for inference with vllm
# If you have more GPUs, change tensor parallel size to GPU numbers you can afford
model = LLM("moreh/Motif-100B-Instruct", tensor_parallel_size=4)
tokenizer = AutoTokenizer.from_pretrained("moreh/Motif-100B-Instruct")
messages = [
    {"role": "system", "content": "You are a helpful assistant"},
    {"role": "user", "content": "유치원생에게 빅뱅 이론의 개념을 설명해보세요"},
]

messages_batch = [tokenizer.apply_chat_template(conversation=messages, add_generation_prompt=True, tokenize=False)]

# vllm does not support generation_config of hf. So we have to set it like below
sampling_params = SamplingParams(max_tokens=512, temperature=0, repetition_penalty=1.0, stop_token_ids=[tokenizer.eos_token_id])
responses = model.generate(messages_batch, sampling_params=sampling_params)

print(responses[0].outputs[0].text)
```

### Use with transformers  
- Minimum requirements: 4xA100 80GB GPUs OR 4xAMD MI250 GPUs
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "moreh/Motif-100B-Instruct"

# all generation configs are set in generation_configs.json
model = AutoModelForCausalLM.from_pretrained(model_id).cuda()
tokenizer = AutoTokenizer.from_pretrained(model_id)
messages = [
    {"role": "system", "content": "You are a helpful assistant"},
    {"role": "user", "content": "유치원생에게 빅뱅 이론의 개념을 설명해보세요"},
]

messages_batch = tokenizer.apply_chat_template(conversation=messages, add_generation_prompt=True, tokenize=False)
input_ids = tokenizer(messages_batch, padding=True, return_tensors='pt')['input_ids'].cuda()

outputs = model.generate(input_ids)
```