Llama-3.2-3B-MoE-4Expert

A 4-expert Mixture of Experts (MoE) model built on unsloth/Llama-3.2-3B-Instruct, specializing in chat, code, creative writing, and mathematics.

Model Description

This model combines four specialized Llama-3.2-3B variants into a single MoE architecture that intelligently routes queries to the most appropriate expert:

General Chat & Explanations - Clear, structured responses for everyday queries
Code & Programming - Python development, algorithms, and optimization
Creative Writing - Stories, poetry, roleplay, and narrative content
Mathematics - Equations, proofs, calculus, and step-by-step solutions

Architecture

Base Model: unsloth/Llama-3.2-3B-Instruct
Gate Mode: cheap_embed (embedding-based routing)
Precision: bfloat16
Merge Method: mergekit-moe
Total Experts: 4

Expert Models

Expert	Model	Specialization
1	unsloth/Llama-3.2-3B-Instruct	General chat, summaries, explanations
2	prithivMLmods/Codepy-Deepthink-3B	Python, algorithms, debugging
3	DavidAU/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored	Creative writing, fiction, roleplay
4	prithivMLmods/Llama-3.2-3B-Math-Oct	Mathematics, proofs, calculations

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "Fu01978/Llama-3.2-3B-MoE-4Expert"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a Python function to calculate fibonacci numbers"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))