SOLAR-10.7x2_19B

🌞🚀 SOLAR-10.7x2_19B

Merge of two Solar-10.7B instruct finetunes.

Performs higher than mistralai/mixtral-8x7b-Instruct-v0.1

🌅 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

model is currently experimental and was evaluated in 4-bit

📚 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}
}