--- license: apache-2.0 tags: - moe - frankenmoe - merge - mergekit - lazymergekit - flemmingmiguel/MBX-7B-v3 - Kukedlc/NeuTrixOmniBe-7B-model-remix - PetroGPT/WestSeverus-7B-DPO - vanillaOVO/supermario_v4 base_model: - flemmingmiguel/MBX-7B-v3 - Kukedlc/NeuTrixOmniBe-7B-model-remix - PetroGPT/WestSeverus-7B-DPO - vanillaOVO/supermario_v4 --- # MixtureofMerges-MoE-4x7b-v4 MixtureofMerges-MoE-4x7b-v4 is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [flemmingmiguel/MBX-7B-v3](https://huggingface.co/flemmingmiguel/MBX-7B-v3) * [Kukedlc/NeuTrixOmniBe-7B-model-remix](https://huggingface.co/Kukedlc/NeuTrixOmniBe-7B-model-remix) * [PetroGPT/WestSeverus-7B-DPO](https://huggingface.co/PetroGPT/WestSeverus-7B-DPO) * [vanillaOVO/supermario_v4](https://huggingface.co/vanillaOVO/supermario_v4) ## 🧩 Configuration ```yaml base_model: Kukedlc/NeuTrixOmniBe-7B-model-remix gate_mode: hidden dtype: bfloat16 experts: - source_model: flemmingmiguel/MBX-7B-v3 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." - "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?" negative_prompts: - "misses key evidence" - "overly general" - "focuses on irrelevant details" - "assumes information not provided" - "relies on stereotypes" - source_model: Kukedlc/NeuTrixOmniBe-7B-model-remix 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." negative_prompts: - "sounds too basic" - "understated" - "dismisses important details" - "avoids the question's nuance" - "takes this statement too literally" - source_model: PetroGPT/WestSeverus-7B-DPO positive_prompts: - "Calculate the answer to this math problem" - "My mathematical capabilities are strong, allowing me to handle complex mathematical queries" - "solve for" - "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: - "incorrect" - "inaccurate" - "creativity" - "assumed without proof" - "rushed calculation" - "confuses mathematical concepts" - "draws illogical conclusions" - "circular reasoning" - source_model: vanillaOVO/supermario_v4 positive_prompts: - "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: - "repetitive phrases" - "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" ``` ## 💻 Usage ```python !pip install -qU transformers bitsandbytes accelerate from transformers import AutoTokenizer import transformers import torch model = "jsfs11/MixtureofMerges-MoE-4x7b-v4" 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"]) ```