metadata

language:
  - en
library_name: transformers
datasets:
  - psmathur/lima_unchained_v1
license: llama2

Lima_Unchained_70b

A Llama2-70b model fine-tuned using QLora on all the linear layers with carefully selected ~900 conversations from the Lima

P.S. If you're interested to collaborate, please connect with me at www.linkedin.com/in/pankajam.

Evaluation

We evaluated Lima_Unchained_70b 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	0.6826	0.0141
hellaswag	acc_norm	0.8765	0.0038
mmlu	acc_norm	0.70	0.0351
truthfulqa_mc	mc2	0.4876	0.0157
Total Average	-	0.6867

Example Usage

Here is the prompt format


### User:
Write a stand-up skit in the style of George Carlin that ridicules Pacific Gas and Electric.

### Assistant:

Below shows a code example on how to use this model

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_path="pankajmathur/Lima_Unchained_70b"

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
  model_path,
  torch_dtype=torch.float16,
  load_in_8bit=True,
  low_cpu_mem_usage=True,
  device_map="auto"
)

#generate text steps
instruction = "Write a stand-up skit in the style of George Carlin that ridicules Pacific Gas and Electric."
prompt = f"### 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{Lima_Unchained_70b,
  author = {Pankaj Mathur},
  title = {Lima_Unchained_70b: A LIMA style Llama2-70b model},
  year = {2023},
  publisher = {HuggingFace},
  journal = {HuggingFace repository},
  howpublished = {\url{https://https://huggingface.co/psmathur/model_42_70b},
}

@misc{ChuntingZhou,
      title={LIMA: Less Is More for Alignment}, 
      author={Chunting Zhou, Pengfei Liu, Puxin Xu, Srini Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu,
      Susan Zhang, Gargi Ghosh, Mike Lewis, Luke Zettlemoyer, Omer Levy},
      year={2023},
      eprint={2305.11206},
      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.	58.2
ARC (25-shot)	68.26
HellaSwag (10-shot)	87.65
MMLU (5-shot)	70.0
TruthfulQA (0-shot)	48.76
Winogrande (5-shot)	83.66
GSM8K (5-shot)	34.72
DROP (3-shot)	14.37