--- license: apache-2.0 tags: - moe - frankenmoe - merge - mergekit - lazymergekit model-index: - name: Lumina-3.5 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 71.59 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Ppoyaa/Lumina-3.5 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 88.82 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Ppoyaa/Lumina-3.5 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.48 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Ppoyaa/Lumina-3.5 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 75.66 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Ppoyaa/Lumina-3.5 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 83.98 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Ppoyaa/Lumina-3.5 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 67.93 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Ppoyaa/Lumina-3.5 name: Open LLM Leaderboard --- # Lumina-3.5 Lumina-3.5 is a Mixture of Experts (MoE) made with [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing) with improved reasoning from [Lumina-3](https://huggingface.co/Ppoyaa/Lumina-3). This model uses a context window of up to 32k. # Response Examples ``` Math Input: 25 - 4 * 2 + 3 = ? ------------------------------------ Output: To solve this equation, we will follow the order of operations (PEMDAS/BODMAS). This means performing any multiplications and divisions first from left to right, and then additions and subtractions from left to right. The given equation is: 25 - 4 * 2 + 3 First, let's perform the multiplication: 4 * 2 = 8 Now, our equation looks like: 25 - 8 + 3 Next, perform the subtraction: 25 - 8 = 17 Finally, do the addition: 17 + 3 = 20 So, the result of the equation 25 - 4 * 2 + 3 is 20. ``` ``` Reasoning Input: Jane is faster than Joe. Joe is faster than Sam. Is Sam faster than Jane? Explain your reasoning step by step. ------------------------------------ Output: To answer this question, let's analyze the given information step by step: We are provided with a comparison between three individuals - Jane, Joe, and Sam - in terms of their relative speeds. The statements tell us the following about their ranks: Jane > Joe (Jane is faster than Joe) Joe > Sam (Joe is faster than Sam) Now we need to determine if the opposite relationship holds true: whether Sam is faster than Jane or not. Based on the given comparisons, we know that both Jane and Joe have faster speeds compared to Sam. There isn't any piece of information suggesting otherwise. Since the original premise established an ordered ranking among these individuals (Jane > Joe > Sam), and the direct opposite of the relationship between Sam and the other two is not present, we cannot conclude that Sam is faster than Jane. Thus, based on the provided information, our reasoning confirms that Sam is not faster than Jane. ``` ## 🏆 Open LLM Leaderboard Evaluation Results | Metric |Value| |---------------------------------|----:| |Avg. |75.41| |AI2 Reasoning Challenge (25-Shot)|71.59| |HellaSwag (10-Shot) |88.82| |MMLU (5-Shot) |64.48| |TruthfulQA (0-shot) |75.66| |Winogrande (5-shot) |83.98| |GSM8k (5-shot) |67.93| # Quants Special thanks to GGUFs made by [mradermacher](https://huggingface.co/mradermacher) * [mradermacher/Lumina-3.5-GGUF](https://huggingface.co/mradermacher/Lumina-3.5-GGUF) ## 💻 Usage ```python !pip install -qU transformers bitsandbytes accelerate from transformers import AutoTokenizer import transformers import torch model = "Ppoyaa/Lumina-3.5" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True}, ) messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}] prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Ppoyaa__Lumina-3.5) | Metric |Value| |---------------------------------|----:| |Avg. |75.41| |AI2 Reasoning Challenge (25-Shot)|71.59| |HellaSwag (10-Shot) |88.82| |MMLU (5-Shot) |64.48| |TruthfulQA (0-shot) |75.66| |Winogrande (5-shot) |83.98| |GSM8k (5-shot) |67.93|