Jerboa
Lightweight Language & Multimodal Model (in active development)
Jerboa is an ultra-lightweight language and multimodal model (~221M parameters) featuring a 28-layer Deep & Thin architecture optimized for Apple Silicon (MPS / Metal) and edge deployments.
Model Specifications
| Attribute | Specification |
|---|---|
| Language Backbone | JerboaForCausalLM (~221M parameters) |
| Multimodal Model | JerboaVLForConditionalGeneration |
| Layers & Hidden Dim | 28 Layers, $d_{model}=768$, $d_{ffn}=2048$ (SwiGLU) |
| Attention | GQA (12 Query : 4 KV heads), QK-Norm, Interleaved SWA (window 2048, global every 4 layers) |
| Vocabulary & RoPE | 49,152 BPE Vocab, RoPE $ heta=500,000$ |
| Pre-training Volume | > 1.23 Billion tokens (75% FineWeb-Edu, 25% Python Code) |
| Hardware Target | Apple Silicon (MPS / Metal) & Edge Devices |
Quickstart
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "ztor2/jerboa-base"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
prompt = "Hello, what are you?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))