--- license: apache-2.0 datasets: - Intel/orca_dpo_pairs language: - en library_name: transformers --- # 🌞🚀 SOLAR-4x10.7_36B Merge of four Solar-10.7B instruct finetunes. ![solar](solar.png) ## 🌟 Usage ## 🌅 Code Example Example also available in [colab](https://colab.research.google.com/drive/10FWCLODU_EFclVOFOlxNYMmSiLilGMBZ?usp=sharing) ```python from transformers import AutoModelForCausalLM, AutoTokenizer def generate_response(prompt): """ Generate a response from the model based on the input prompt. Args: prompt (str): Prompt for the model. Returns: str: The generated response from the model. """ # Tokenize the input prompt inputs = tokenizer(prompt, return_tensors="pt") # Generate output tokens outputs = model.generate(**inputs, max_new_tokens=512, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.pad_token_id) # Decode the generated tokens to a string response = tokenizer.decode(outputs[0], skip_special_tokens=True) return response # Load the model and tokenizer model_id = "macadeliccc/Orca-SOLAR-4x10.7b" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True) prompt = "Explain the proof of Fermat's Last Theorem and its implications in number theory." print("Response:") print(generate_response(prompt), "\n") ``` ## Llama.cpp GGUF Quants available [here]() ![llama.cpp-screenshot](orca-llama-cpp-1.png) ## Evaluations TODO ### 📚 Citations ```bibtex @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} } ```