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Llama-3-MAAL-8B-Instruct-v0.1-GGUF

Name: Llama-3-MAAL-8B-Instruct-v0.1-GGUF
Availability: InStock

meta-llama/Meta-Llama-3-8B-Instruct

QuantFactory/Llama-3-MAAL-8B-Instruct-v0.1-GGUF

This is quantized version of maum-ai/Llama-3-MAAL-8B-Instruct-v0.1 created using llama.cpp

Original Model Card

Llama-3-MAAL-8B-Instruct-v0.1

we release MAAL, Multilingual Adaptive Augmentation Language-model which comprises a groundbreaking fusion of multilingual capabilities and adaptive augmentation techniques.

Developed by: maum.ai Brain NLP. Jaeyoon Jung, Jinjoo Lee, Yongjae Lee, Dongjun Lee, Woosung Joo
Language(s) (NLP): Korean, English (currently, bilingual)

Model Description

Version 0.1 uses cross-lingual training to transfer instruction-following capabilities from English to Korean.

We Trained this model on an 8 H100-80G for 1 day with cross-lingual training dataset
we recommend using the fixed system prompt for the model unless you fine-tune it

너는 마음에이아이의 챗봇 MAAL이다. 고객의 질문에 친절하게 답하여라.

sample inference code (GPU)

import transformers
import torch

model_id = "maum-ai/Llama-3-MAAL-8B-Instruct-v0.1"
model = transformers.AutoModelForCausalLM.from_pretrained(model_id).to("cuda")
tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
streamer = transformers.TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

# we recommend using the fixed prompt for the model unless you fine-tune it
prompt = "너는 마음에이아이의 챗봇 MAAL이다. 고객의 질문에 친절하게 답하여라."
instruction = "사과 한 박스에는 사과가 30개 들어있는데, 처음에는 사과 3박스가 있었고, 내가 사과 5개를 먹었어. 남은 사과는 총 몇개야?"

messages = [
    {"role": "system", "content": f"{prompt}"},
    {"role": "user", "content": f"{instruction}"}
    ]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    return_tensors='pt').to("cuda")
outputs = model.generate(inputs, streamer=streamer, max_new_tokens=1024, pad_token_id=tokenizer.eos_token_id)

Evaluation Results

As the main goal of version 0.1 is to transfer instruction-following capabilities from English to Korean without utilizing continuous pre-training, etc., we select LogicKor as our evaluation method to assess the Korean instruction skills.

We compare our model with a similar parameter model (less than 13B) that has been fine-tuned on the Korean dataset. * denotes our self-report result.

Model	single-turn(↑)	multi-turn(↑)	average(↑)
maum-ai/Llama-3-MAAL-8B-Instruct-v0.1*	5.80	4.66	5.23
maywell/Synatra-kiqu-10.7B	5.71	4.73	5.22
yanolja/EEVE-Korean-Instruct-10.8B-v1.0	5.78	3.92	4.85
nlpai-lab/KULLM3	4.61	4.83	4.72
MLP-KTLim/llama3-Bllossom*	2.11	1.57	1.84

Limitations

Due to this model being trained on a small dataset, it has several limitations.

Hard to generate diverse Korean texts
lack of Korean knowledge & Culture (localization)
Not work with Image inputs and video inputs

Todo

we will solve these limitations one by one by upgrading this model like as...

Enhance the Korean generation through Vocabulary Expansion & Continuous pre-training. (more Korean corpus!)
Localize with cultural adaptation method and additional Korean knowledge data. similar idea
Develop a Vision Language Model that can handle both video and image inputs. similar idea