femiari/Llama3MoE

QwenMoEAriel

QwenMoEAriel is a Mixture of Experts (MoE) made with the following models using LazyMergekit:

• meta-llama/Meta-Llama-3-8B-Instruct
• rombodawg/Llama-3-8B-Instruct-Coder

🧩 Configuration

base_model: meta-llama/Meta-Llama-3-8B-Instruct
experts:
  - source_model: meta-llama/Meta-Llama-3-8B-Instruct
    positive_prompts:
    - "explain"
    - "chat"
    - "assistant"
    - "think"
    - "roleplay"
    - "versatile"
    - "helpful"
    - "factual"
    - "integrated"
    - "adaptive"
    - "comprehensive"
    - "balanced"
    negative_prompts:
    - "specialized"
    - "narrow"
    - "focused"
    - "limited"
    - "specific"
  - source_model: rombodawg/Llama-3-8B-Instruct-Coder
    positive_prompts:
    - "python"
    - "math"
    - "solve"
    - "code"
    - "programming"
    - "javascript"
    - "algorithm"
    - "factual"
    negative_prompts:
    - "sorry"
    - "cannot"
    - "concise"
    - "imaginative"
    - "creative"

💻 Usage

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "femiari/QwenMoEAriel"

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"])