orca_mini_v3_13b
A Llama2-13b model trained on Orca Style datasets.
Passionate about Generative AI? I help companies to privately train and deploy custom LLM/MLLM affordably. For startups, I can even assist with securing GPU grants to get you started. Let's chat!https://www.linkedin.com/in/pankajam Looking forward to connecting!
quantized versions
Big thanks to @TheBloke
license disclaimer:
This model is bound by the license & usage restrictions of the original Llama-2 model. And comes with no warranty or gurantees of any kind.
Evaluation
We evaluated orca_mini_v3_13b on a wide range of tasks using Language Model Evaluation Harness from EleutherAI.
Here are the results on metrics used by HuggingFaceH4 Open LLM Leaderboard
Task | Metric | Value | Stderr |
arc_challenge | acc_norm | 0.6314 | 0.0141 |
hellaswag | acc_norm | 0.8242 | 0.0038 |
mmlu | acc_norm | 0.5637 | 0.0351 |
truthfulqa_mc | mc2 | 0.5127 | 0.0157 |
Total Average | - | 0.6329877193 |
Example Usage
Here is the prompt format
### System:
You are an AI assistant that follows instruction extremely well. Help as much as you can.
### User:
Tell me about Orcas.
### Assistant:
Below shows a code example on how to use this model
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
tokenizer = AutoTokenizer.from_pretrained("psmathur/orca_mini_v3_13b")
model = AutoModelForCausalLM.from_pretrained(
"psmathur/orca_mini_v3_13b",
torch_dtype=torch.float16,
load_in_8bit=True,
low_cpu_mem_usage=True,
device_map="auto"
)
system_prompt = "### System:\nYou are an AI assistant that follows instruction extremely well. Help as much as you can.\n\n"
#generate text steps
instruction = "Tell me about Orcas."
prompt = f"{system_prompt}### User: {instruction}\n\n### Assistant:\n"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
output = model.generate(**inputs, do_sample=True, top_p=0.95, top_k=0, max_new_tokens=4096)
print(tokenizer.decode(output[0], skip_special_tokens=True))
Limitations & Biases:
While this model aims for accuracy, it can occasionally produce inaccurate or misleading results.
Despite diligent efforts in refining the pretraining data, there remains a possibility for the generation of inappropriate, biased, or offensive content.
Exercise caution and cross-check information when necessary.
Citiation:
Please kindly cite using the following BibTeX:
@misc{orca_mini_v3_13b,
author = {Pankaj Mathur},
title = {orca_mini_v3_13b: An Orca Style Llama2-70b model},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://https://huggingface.co/psmathur/orca_mini_v3_13b},
}
@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@software{touvron2023llama2,
title={Llama 2: Open Foundation and Fine-Tuned Chat Models},
author={Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava,
Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller,
Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez Madian Khabsa, Isabel Kloumann,
Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov,
Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith,
Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu , Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan,
Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom},
year={2023}
}
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 52.23 |
ARC (25-shot) | 63.14 |
HellaSwag (10-shot) | 82.35 |
MMLU (5-shot) | 56.52 |
TruthfulQA (0-shot) | 51.81 |
Winogrande (5-shot) | 76.48 |
GSM8K (5-shot) | 13.12 |
DROP (3-shot) | 22.23 |
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 57.24 |
AI2 Reasoning Challenge (25-Shot) | 63.14 |
HellaSwag (10-Shot) | 82.35 |
MMLU (5-Shot) | 56.52 |
TruthfulQA (0-shot) | 51.81 |
Winogrande (5-shot) | 76.48 |
GSM8k (5-shot) | 13.12 |
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 15.00 |
IFEval (0-Shot) | 28.97 |
BBH (3-Shot) | 25.55 |
MATH Lvl 5 (4-Shot) | 1.89 |
GPQA (0-shot) | 2.01 |
MuSR (0-shot) | 17.11 |
MMLU-PRO (5-shot) | 14.50 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard63.140
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard82.350
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard56.520
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard51.810
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard76.480
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard13.120
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard28.970
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard25.550
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard1.890
- acc_norm on GPQA (0-shot)Open LLM Leaderboard2.010