MOE-SWE-DAN-NO-CODE

MOE-SWE-DAN-NO-CODE

MOE-SWE-DAN-NO-CODE is a Mixure of Experts (MoE) made with the following models using LazyMergekit:

• RJuro/munin-neuralbeagle-7b
• timpal0l/BeagleCatMunin
• birgermoell/Munin-NeuralBeagle-NorskGPT
• teknium/OpenHermes-2.5-Mistral-7B

🧩 Configuration

base_model: RJuro/munin-neuralbeagle-7b
dtype: float16
gate_mode: cheap_embed
experts:
  - source_model: RJuro/munin-neuralbeagle-7b
    positive_prompts: ["You are a helpful Danish assistant."]
  - source_model: timpal0l/BeagleCatMunin
    positive_prompts: ["You are a helpful Swedish assistant."]
  - source_model: birgermoell/Munin-NeuralBeagle-NorskGPT 
    positive_prompts: ["You are a helpful Norwegian assistant."]
  - source_model: teknium/OpenHermes-2.5-Mistral-7B
    positive_prompts: ["You are a helpful coding assistant."]

💻 Usage

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "merge-crew/MOE-SWE-DAN-NO-CODE"

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"])