--- license: apache-2.0 tags: - moe - frankenmoe - merge - mergekit - lazymergekit - Gille/StrangeMerges_32-7B-slerp - mlabonne/AlphaMonarch-7B base_model: - Gille/StrangeMerges_32-7B-slerp - mlabonne/AlphaMonarch-7B model-index: - name: MixtureofMerges-MoE-2x7b-v7 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: 73.21 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-v7 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: 89.05 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-v7 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: 64.63 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-v7 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: 78.34 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-v7 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: 84.93 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-v7 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: 69.07 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=jsfs11/MixtureofMerges-MoE-2x7b-v7 name: Open LLM Leaderboard --- # MixtureofMerges-MoE-2x7b-v7 MixtureofMerges-MoE-2x7b-v7 is a Mixture of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Gille/StrangeMerges_32-7B-slerp](https://huggingface.co/Gille/StrangeMerges_32-7B-slerp) * [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B) ## 🧩 Configuration ```yaml base_model: Gille/StrangeMerges_32-7B-slerp gate_mode: hidden dtype: bfloat16 experts: - source_model: Gille/StrangeMerges_32-7B-slerp positive_prompts: - "Answer this question from the ARC (Argument Reasoning Comprehension)." - "Use common sense and logical reasoning skills." - "What assumptions does this argument rely on?" - "Are these assumptions valid? Explain." - "Analyze the logical structure of this argument. Identify the premises, conclusion, and any assumptions made" - "Identify any potential counterarguments to this position. How might someone challenge the reasoning presented?" - "Could this be explained in a different way? Provide an alternative explanation." - "Identify any weaknesses in this argument." - "Does this argument contain any logical fallacies? If so, which ones?" - "Generate a few possible continuations to this scenario." - "Demonstrate understanding of everyday commonsense in your response." - "Use contextual clues to determine the most likely outcome." - "Continue this scenario, but make the writing style sound archaic and overly formal." - "This narrative is predictable. Can you introduce an unexpected yet plausible twist?" - "The character is angry. Continue this scenario showcasing a furious outburst." negative_prompts: - "misses key evidence" - "overly general" - "commits the fallacy of hasty generalization" - "focuses on irrelevant details" - "assumes information not provided" - "relies on stereotypes" - "repetitive phrases" - "engages in circular reasoning" - "overuse of the same words" - "contradicts earlier statements - breaks the internal logic of the scenario" - "out of character dialogue" - "awkward phrasing - sounds unnatural" - "doesn't match the given genre" - source_model: mlabonne/AlphaMonarch-7B positive_prompts: - "Answer this question, demonstrating commonsense understanding and using any relevant general knowledge you may have." - "Provide a concise summary of this passage, then explain why the highlighted section is essential to the main idea." - "Read these two brief articles presenting different viewpoints on the same topic. List their key arguments and highlight where they disagree." - "Paraphrase this statement, changing the emotional tone but keeping the core meaning intact. Example: Rephrase a worried statement in a humorous way" - "Create a short analogy that helps illustrate the main concept of this article." - "Explain the concept of physics to a high school student. Use analogies and examples to clarify the main ideas." - "Calculate the answer to this math problem" - "My mathematical capabilities are strong, allowing me to handle complex mathematical queries" - "solve for" - "Analyze the given data and identify any patterns or trends. What conclusions can be drawn from this information?" - "A store sells apples at $0.50 each. If Emily buys 12 apples, how much does she need to pay?" - "Isolate x in the following equation: 2x + 5 = 17" - "Solve this equation and show your working." - "Explain why you used this formula to solve the problem." - "Attempt to divide this number by zero. Explain why this cannot be done." negative_prompts: - "sounds too basic" - "understated" - "dismisses important details" - "avoids the question's nuance" - "skips essential steps in the solution" - "takes this statement too literally" - "incorrect" - "inaccurate" - "assumed without proof" - "uses jargon without explanation" - "rushed calculation" - "confuses mathematical concepts" - "draws illogical conclusions" - "circular reasoning" ``` ## 💻 Usage ```python !pip install -qU transformers bitsandbytes accelerate from transformers import AutoTokenizer import transformers import torch model = "jsfs11/MixtureofMerges-MoE-2x7b-v7" 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_jsfs11__MixtureofMerges-MoE-2x7b-v7) | Metric |Value| |---------------------------------|----:| |Avg. |76.54| |AI2 Reasoning Challenge (25-Shot)|73.21| |HellaSwag (10-Shot) |89.05| |MMLU (5-Shot) |64.63| |TruthfulQA (0-shot) |78.34| |Winogrande (5-shot) |84.93| |GSM8k (5-shot) |69.07|