metadata

license: apache-2.0
language:
  - en
library_name: transformers

🌞🚀 SOLAR-math-2x10.7_19B

Merge of two SOLAR models. This is an experiment to improve models ability to learn math and retain other skills.

$solar$

🌅 Code Example

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("macadeliccc/SOLAR-math-2x10.7b",load_in_4bit=True)
model = AutoModelForCausalLM.from_pretrained(
    "macadeliccc/SOLAR-math-2x10.7b",
    device_map="auto",
    torch_dtype=torch.float16,
)

conversation = [ {'role': 'user', 'content': 'A rectangle has a length that is twice its width and its area is 50 square meters. Find the dimensions of the rectangle.'} ] 

prompt = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)

inputs = tokenizer(prompt, return_tensors="pt").to(model.device) 
outputs = model.generate(**inputs, use_cache=True, max_length=4096)
output_text = tokenizer.decode(outputs[0]) 
print(output_text)

Evaluations

TODO

📚 Citations

@misc{kim2023solar,
      title={SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling}, 
      author={Dahyun Kim and Chanjun Park and Sanghoon Kim and Wonsung Lee and Wonho Song and Yunsu Kim and Hyeonwoo Kim and Yungi Kim and Hyeonju Lee and Jihoo Kim and Changbae Ahn and Seonghoon Yang and Sukyung Lee and Hyunbyung Park and Gyoungjin Gim and Mikyoung Cha and Hwalsuk Lee and Sunghun Kim},
      year={2023},
      eprint={2312.15166},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}