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RichardErkhov/allknowingroger_-_MultiverseMath-12B-MoE-gguf

sourceHugging Faceupdated 2y agoView on Hugging Face
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Quantization made by Richard Erkhov.

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MultiverseMath-12B-MoE - GGUF

  • —Model creator: https://huggingface.co/allknowingroger/
  • —Original model: https://huggingface.co/allknowingroger/MultiverseMath-12B-MoE/

Original model description: --- license: apache-2.0 tags:

  • —moe
  • —frankenmoe
  • —merge
  • —mergekit
  • —lazymergekit
  • —allknowingroger/MultiverseEx26-7B-slerp
  • —DT12the/Math-Mixtral-7B base_model:
  • —allknowingroger/MultiverseEx26-7B-slerp
  • —DT12the/Math-Mixtral-7B ---

NeuralPipe-7B-slerp

NeuralPipe-7B-slerp is a Mixture of Experts (MoE) made with the following models using LazyMergekit:

🧩 Configuration

yaml
base_model: allknowingroger/MultiverseEx26-7B-slerp
experts:
  - source_model: allknowingroger/MultiverseEx26-7B-slerp
    positive_prompts: ["what"]
  - source_model: DT12the/Math-Mixtral-7B
    positive_prompts: ["math"]

💻 Usage

python
!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "allknowingroger/MultiverseMath-12B-MoE"

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"])