asyafiqe/Merak-7B-v3-Mini-Orca-Indo

🦚Merak-7B-v3-Mini-Orca🐳

Merak-7B-v3-Mini-Orca is Ichsan2895's Merak-7B-v3 fine-tuned on Bahasa Indonesia translated psmathur's orca_mini_v1_dataset.

Usage

This model fit on 16GB VRAM GPU (Google Collab T4 wil do), by using BitsandBytes it can run on 6GB VRAM GPU.

Quantized versions is available:

GPTQ: https://huggingface.co/asyafiqe/Merak-7B-v3-Mini-Orca-Indo-GPTQ

GGML/GGUF: I will try to make this version once GGUF merge is stable.

Start chatting with Merak Mini Orca using the following code snippet:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
tokenizer = AutoTokenizer.from_pretrained("asyafiqe/Merak-7B-v3-Mini-Orca-Indo")
model = AutoModelForCausalLM.from_pretrained("asyafiqe/Merak-7B-v3-Mini-Orca-Indo", torch_dtype=torch.float16, device_map="auto")

system_prompt = "SYSTEM: 'Anda adalah asisten AI. Anda akan diberi tugas. Anda harus menghasilkan jawaban yang rinci dan panjang.\n"

message = "Buatlah rencana untuk mengurangi penggunaan listrik di rumah."

prompt = f"{system_prompt}USER: {message}\nASSISTANT:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
output = model.generate(**inputs, do_sample=True, temperature=0.1, max_new_tokens=200)

print(tokenizer.decode(output[0], skip_special_tokens=True))

Prompt format

You can use Vicuna 1.1 format for Ooobabooga's text generation webui.

SYSTEM: Anda adalah asisten AI. Anda akan diberi tugas. Anda harus memberikan jawaban yang rinci dan panjang.
USER: <prompt> (without the <>)
ASSISTANT:

Training details

Merak-7B-v3-Mini-Orca was instruction fine-tuned on 2 x 3090-24GB for 6 hours. LoRA, DeepSpeed ZeRO-2, and FlashAttention were implemented during training using Axolotl.

Hyperparameter	value
learning rate	0.0004
batch size	16
microbatch size	2
warmup step	100
epochs	2
weight decay	0.0
lr scheduler	cosine
lora alpha	16
lora rank	16
lora dropout	0.05
lora target modules	q_proj, v_proj, k_proj, o_proj
cutoff length	4096

Training loss

Step	Train Loss
1	0.9578
100	0.816
200	0.7819
300	0.7279
400	0.732
500	0.7139
600	0.6829
700	0.6641
800	0.6553

Limitations and bias

Llama 2 and fine-tuned variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2 and any fine-tuned varient's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2 variants, developers should perform safety testing and tuning tailored to their specific applications of the model.

Please see the Responsible Use Guide available at https://ai.meta.com/llama/responsible-use-guide/

Citation

@Paper{arXiv,
  author  = {Touvron, et al},
  title   = {Llama 2: Open Foundation and Fine-Tuned Chat Models},
  journal = {arXiv preprint arXiv:2307.09288},
  year    = {2023}
}
@misc{orca_mini_v3_70b,
  author = {Pankaj Mathur},
  title = {orca_mini_v3_70b: An Orca Style Llama2-70b model},
  year = {2023},
  publisher = {HuggingFace},
  journal = {HuggingFace repository},
  howpublished = {\url{https://https://huggingface.co/psmathur/orca_mini_v3_70b},
}
@article{hu2021lora,
  title={LoRA: Low-Rank Adaptation of Large Language Models},
  author={Hu, Edward J. and Shen, Yelong and Wallis, Phillip and Allen-Zhu, Zeyuan and Li, Yuanzhi and Wang, Shean and Chen, Weizhu},
  journal={CoRR},
  year={2021}
}