ZeusMM
ZeusMM is a decoder-only multimodal conversational LM with:
- Role-aware RoPE + KV cache
- Dual fusion (Cross-Attn + FiLM) with a learned router
- Modality-aware MoE-MLP
- Drop-in vision (CLIP), audio (Wav2Vec2), retrieval (any HF encoder)
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "Wonder-Griffin/ZeusMM"
tok = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(repo, trust_remote_code=True)
prompt = "<|system|>You are Zeus.<|end|>\n<|user|>Say hi.<|end|>\n<|assistant|>"
x = tok(prompt, return_tensors="pt")
y = model.generate(**x, max_new_tokens=60, do_sample=True, top_p=0.9, temperature=0.9)
print(tok.decode(y[0], skip_special_tokens=False))
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## Uses
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## How to Get Started with the Model
Use the code below to get started with the model.
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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