Mira-v1

Fast, honest, open typed decisions

Benchmarks · Quickstart · API · Limitations

What is this?

Mira turns state + typed questions → probability distributions in a single forward pass. No text generation. Ask choice, score, or noul questions — alone or batched — and get calibrated probabilities, confidence, and an abstain signal your code can branch on.

from transformers import AutoModel, AutoTokenizer
import torch
tok = AutoTokenizer.from_pretrained("sagea-ai/Mira-v1")
enc = AutoModel.from_pretrained("sagea-ai/Mira-v1").eval()
heads = torch.load(__import__("huggingface_hub").hf_hub_download(
    "sagea-ai/Mira-v1", "heads.pt"), map_location="cpu", weights_only=True)
state, opts = "My payouts have been failing for 3 days.", ["billing", "technical", "sales"]
hs = enc(**tok([state + " [SEP] " + o for o in opts],
               padding=True, return_tensors="pt")).last_hidden_state[:, 0]
probs = ((hs @ heads["scorer"]["weight"].T).squeeze(-1) / 1.05).softmax(-1)
print(dict(zip(opts, [round(float(p), 3) for p in probs])))

Or serve it TypeSafe-compatible: POST /v1/systemone — same shape, swap the base URL.

Evaluation

Measured on commodity CPU (i5-1335U), one harness for every open model.

Benchmark	Mira-v1	Julia 1	Jev
Typed decisions (2000)	0.745	0.726	0.727
ECE (15-bin)	0.025	0.234	—
Confident-error (≥0.9)	0.006	0.234	—
AG News (100)	0.25	0.94	0.91
Emotion (100)	0.87	0.86	0.48
Banking77 (100, native full-label)	0.93	0.64 (72-label shortlist variant)	0.87
Short-query p50	30 ms	321 ms	70–500 ms API

Mira ties Julia on typed decisions, leads the open class on calibration by an order of magnitude, and routes intents better — while losing clearly on topic and emotion classification. The AG News / Emotion / Banking77 rows for Julia and Jev are supplier-reported figures. Mira's own pilots are measured here on box-01 (100 cases each, same protocol family): topic and emotion classification are mixed (AG News 0.30 remains a weakness — Mira is a routing specialist, not a topic model; Emotion 0.87 matches Julia after diverse-data training), while intent-style Banking77 at native full-label resolution is a strength (0.93 vs Julia's 0.64 shortlist variant). Knowledge-heavy and 70-label comparisons are Julia/Jev territory today — stated, not hidden. Nepali,

romanized Nepali, and code-mixed slices are Mira-first reporting no other small model publishes.

Limitations

Mira compares answers you provide — it cannot supply missing facts or do arithmetic. Severity direction and urgency separation are the softest slices. Past 20 options a two-stage shortlist narrows then decides (recall reported). Score the exact questions you plan to use before consequential actions. Training data is private; eval harness and predictions are public.

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