license: apache-2.0
library_name: ecoaco
pipeline_tag: text-generation
language:
- en
tags:
- ecoaco
- rurtech
- mixture-of-experts
- moe
- sparse-moe
- banking
- finance
- fintech
- causal-lm
- india
model_name: ECOACO
ECOACO — the rurtech.ai Mixture-of-Experts LLM for banking
ECOACO (Sanskrit: wealth, prosperity, purpose) is rurtech.ai's vertical Mixture-of-Experts language model for banking and finance. It is a real, from-scratch sparse-MoE causal transformer — each block routes every token to the top-2 of 8 expert FFNs, so the model holds far more capacity than it activates per token.
Repo id: rurtech010101/ECOACO · search Hugging Face for “ECOACO MoE”.
This checkpoint is a reference / demonstration model trained at small scale. The architecture is production-faithful — scaling up is a config change (
dim,n_layers,n_experts,vocab_size) plus a real pretraining corpus and GPU budget. It is not a production banking model and must not be used for real financial decisions.
Load it in one line (Python)
# pip install torch safetensors huggingface_hub
from modeling_ecoaco import EcoacoForCausalLM
model, tok = EcoacoForCausalLM.from_pretrained("rurtech010101/ECOACO")
print(model.chat(tok, "The Layer-0 action gate", max_new_tokens=60))
from_pretrained accepts a local folder or a Hugging Face repo id. The
modeling_ecoaco.py and tokenizer.py files ship in the model repo, so the
class is available wherever the weights are.
Architecture
| Name | ECOACO 1.0 |
| Type | Sparse Mixture-of-Experts, decoder-only |
| Total parameters | ~20.7M (reference checkpoint) |
| Active parameters / token | ~6.5M (top-2 of 8 experts) |
| Layers | 6 · Hidden 256 · Heads 8 |
| Experts / layer | 8, top-2 routed |
| Positional encoding | Rotary (RoPE) |
| Normalization | RMSNorm |
| FFN | SwiGLU experts |
| Router aux loss | Switch-Transformer load balancing |
| Tokenizer | Byte-level BPE (no <unk>) |
| Context length | 512 |
model_type |
ecoaco |
Files
config.json HF-style config (model_type: ecoaco)
model.safetensors weights
tokenizer.json byte-level BPE tokenizer
generation_config.json default sampling params
modeling_ecoaco.py the model definition (+ from_pretrained)
tokenizer.py the tokenizer implementation
Ollama
python export_gguf.py --artifacts artifacts --out ecoaco.gguf
ollama create ecoaco -f Modelfile
ollama run ecoaco
Reproduce training
python train.py --steps 400 # byte-BPE + AdamW on the banking corpus
python generate.py --prompt "Safety before autonomy"
The corpus is the rurtech.ai platform's own documentation, so ECOACO learns to speak about the runtime, the Layer-0 action gate, guardrails, and sovereign deployment.
Intended use and limitations
- Intended: demonstrating the ECOACO MoE architecture and the train → package → publish (Hugging Face / git / Ollama) pipeline; a base for scaled-up domain pretraining.
- Not intended: any real banking, credit, compliance, or customer-facing decision. In the rurtech.ai platform every model output flows through the Layer-0 action gate and human approval.
Citation
@software{ecoaco_moe_2026,
title = {ECOACO: a Mixture-of-Experts LLM for banking},
author = {rurtech.ai},
year = {2026},
url = {https://huggingface.co/rurtech010101/ECOACO}
}
License
Apache-2.0.