README.md

metadata

license: apache-2.0
tags:
  - moe
  - frankenmoe
  - merge
  - mergekit
  - lazymergekit
  - allknowingroger/StarlingMaxLimmy2-7B-slerp
  - Kukedlc/NeuralMaths-Experiment-7b
base_model:
  - allknowingroger/StarlingMaxLimmy2-7B-slerp
  - Kukedlc/NeuralMaths-Experiment-7b

StarlingMaths-12B-MoE

StarlingMaths-12B-MoE is a Mixture of Experts (MoE) made with the following models using LazyMergekit:

• allknowingroger/StarlingMaxLimmy2-7B-slerp
• Kukedlc/NeuralMaths-Experiment-7b

🧩 Configuration

base_model: allknowingroger/StarlingMaxLimmy2-7B-slerp
experts:
  - source_model: allknowingroger/StarlingMaxLimmy2-7B-slerp
    positive_prompts: ["why"]
  - source_model: Kukedlc/NeuralMaths-Experiment-7b
    positive_prompts: ["math"]

💻 Usage

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "allknowingroger/StarlingMaths-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"])