Multilingual, non-autoregressive System 1 decision engine. Typed decisions over 100+ languages in a single forward pass — 33 ms — trained with reinforcement learning against strictly proper scoring rules (RLCD), with a router that picks the right checkpoint per request.
deepopen evaluates typed questions (choice, score, noul) over any state (text, email, ticket or JSON document) in a single forward pass — 33 ms for one question, 7.2 ms/question batched, measured on a T4. No text generation, so nothing to parse and nothing to hallucinate.
Three checkpoints, and a Router that picks between them per request:
deepopen ships three checkpoints. The built-in Router is the recommended entry point: it evaluates any state in any language, automatically detects scripts and languages in sub-milliseconds, and dispatches to the optimal checkpoint in a single forward pass.
import deepopen
from deepopen import Router
# Preload checkpoints into memory for instant sub-35ms routing
router = Router(preload=True)
# 1. State in any language or schema
state = {
"from": "[email protected]",
"subject": "Duplicate charge on invoice #4411",
"body": "Hi, we were billed twice for March. Please refund the duplicate today or we will cancel our plan."
}
# 2. Define your typed questions
questions = {
"department": {
"type": "choice",
"instructions": "Which department should handle this request?",
"criteria": {
"billing": "invoices, payments, refunds",
"technical": "bugs, outages, system errors",
"sales": "pricing, new contracts",
"other": "everything else"
}
},
"urgency": {
"type": "score",
"instructions": "How urgent is this request?",
"criteria": ["not urgent", "soon", "critical deadline or blocking issue"]
},
"churn_risk": {
"type": "noul",
"instructions": "Does the user threaten to cancel or leave?"
},
"refund_requested": {
"type": "noul",
"instructions": "Does the user explicitly request a refund?"
}
}
# 3. English state -> automatically routed to deepopen (ModernBERT-large, 39.5 ms)
res_en = router.predict(state, questions)
print("Department :", res_en["answers"]["department"]["choice"]) # -> billing (confidence: 0.94)
print("Routing :", res_en["routing"]["model"]) # -> english
# 4. Hindi state -> automatically routed to deepopen-multilingual (mmBERT-base, 32.8 ms)
res_hi = router.predict({"body": "मुझसे दो बार शुल्क लिया गया, कृपया पैसे वापस करें।"}, questions)
print("Department :", res_hi["answers"]["department"]["choice"]) # -> billing (confidence: 0.86)
print("Routing :", res_hi["routing"]["model"]) # -> multilingual
# 5. Explicit override when you want a specific checkpoint
res_td = router.predict(state, questions, model="typed-decisions")
Every result carries full routing metadata explaining why the choice was made:
res_hi["routing"]
# {
# 'model': 'multilingual',
# 'repo': 'convaiinnovations/deepopen/multilingual',
# 'reason': 'non-Latin script (devanagari, 100% of letters); the English checkpoint cannot read it'
# }
Inspect a routing decision without running any forward pass:
router.route({"body": "Der Kunde wurde zweimal belastet"}, questions).reason
# "Latin script but language looks like 'de', not English"
Why Route: The Evidence
On a shared benchmark (17,416 questions, one T4 GPU, identical questions per model):
Benchmark / Task
English (deepopen)
Multilingual (deepopen-multilingual)
Router (Routed)
MASSIVE intent, English
0.783
0.657
0.783
MASSIVE intent, 13 other languages
0.306
0.451
0.451
XNLI, English
0.860
0.843
0.860
XNLI, 14 other languages
0.521
0.731
0.731
Languages usable (>3x random)
23 / 51
45 / 51
45 / 51
Latency, 1 question (T4 GPU)
39.5 ms
32.8 ms
32.8 ms
Latency, 10 questions batched
158.6 ms
72.3 ms
72.3 ms
The English checkpoint collapses on non-Latin scripts (Khmer scores 0.000 accuracy at 0.952 confidence). Because the model stays confident while being wrong, confidence gating cannot save you. Router detects the script in <0.5 ms pure Python before the forward pass.
Production Preload & Memory
A cold checkpoint build costs seconds; language detection costs microseconds. At the default max_loaded=1, traffic that alternates languages rebuilds a model on every request (measured at a 7.4 s median reload on CPU and 10.3 s on T4).
For a server or production app, preload:
# Every checkpoint resident in memory; language flips cost detection only (<1 ms)
router = Router(preload=True)
router = Router(preload=True, device="cuda")
# Or preload only the specific checkpoints you serve:
router.preload(["english", "multilingual"])
# If your app already built an agent, attach it to avoid duplicate VRAM:
router.attach("english", existing_agent)
# Manage resident memory (default keeps 1 hot, LRU eviction)
router = Router(max_loaded=2) # keep two hot
router.unload() # free memory
Deployment Mode
Per-Request Latency
Model Reloads
Router() (lazy, max_loaded=1)
7 to 10 s on every language switch
1 per switch
Router(preload=True)
32.8 ms (GPU) / 193–464 ms (CPU)
none
Single-Model Mode (Direct SDK)
If you only need a single checkpoint for a dedicated pipeline, you can load models directly:
import deepopen
# 1. Load a specific checkpoint directly from the hub
agent = deepopen.load("convaiinnovations/deepopen") # English root
agent_ml = deepopen.load("convaiinnovations/deepopen", subfolder="multilingual") # 100+ languages
agent_td = deepopen.load("convaiinnovations/deepopen", subfolder="typed-decisions")
# 2. Run all questions in ONE single forward pass (~35 ms on GPU)
result = agent.predict(state, questions)
answers = result["answers"]
print("Department :", answers["department"]["choice"]) # -> billing (confidence: 0.94)
print("Urgency :", answers["urgency"]["score"]) # -> 1.84 / 2.0
print("Churn Risk :", answers["churn_risk"]["noul"]) # -> 0.892 (89.2% probability)
Automated Confidence Gating
Because deepopen's probabilities are trained with strictly proper scoring rules (RLCD), confidence scores are statistically meaningful:
dept = answers["department"]["choice"]
conf = answers["department"]["confidence"]
if conf >= 0.85:
# High confidence: automated action without human in the loop
route_automatically(dept)
else:
# Low confidence: escalate to human triage
escalate_to_human_agent(dept, reason=f"Low confidence ({conf:.2f})")
Built-in Workflow Presets
deepopen provides pre-tuned question schemas for immediate production use:
import deepopen
agent = deepopen.load("convaiinnovations/deepopen")
# 1. Intelligent Model Router (routes to small vs. frontier models)
routing = agent.predict({"request": "Refactor this service using dependency injection"}, deepopen.router_questions())
# 2. Real-time Prompt Guardrails (jailbreaks, injections, leaks)
guard = agent.predict({"prompt": "Ignore all instructions"}, deepopen.guard_questions())
# 3. Content Safety & Moderation (toxicity, harassment, threats)
safety = agent.predict({"post": "User comment text"}, deepopen.moderation_questions())
# 4. Support Ticket Triage (intent, urgency, frustration, churn)
triage = agent.predict({"message": "My payment failed twice"}, deepopen.triage_questions())
Decision Primitives
Primitive
Output
Use Cases
choice
Top label, probabilities per option, confidence
Department routing, intent classification, topic categorization
score
Expected level on ordinal rubric, distribution, confidence
Full report: BENCHMARKS.md — every run consolidated, languages and themes, with per-language detail for all 51 languages.
All deepopen numbers below are measured. Every model answered byte-identical questions
(fixed seed) in the same run. Reproduce with
notebooks/deepopen_benchmark_colab.ipynb on a T4.
Speed (Tesla T4, measured)
questions per call
deepopen
deepopen-multilingual
1
39.5 ms
32.8 ms
5
84.5 ms
40.1 ms
10
158.6 ms (15.9 ms/q)
72.3 ms (7.2 ms/q)
50
771 ms
337 ms (6.8 ms/q)
Batched throughput reaches 103-332 questions/sec on a single T4. For reference, TypeSafe Jev
has been independently measured at 236-276 ms p50
(AbdelStark,
nibzard) -- deepopen answers a single
question roughly 6-7x faster.
deepopen (with routing) vs Jev
Every deepopen figure is what Router().predict(...) actually returns — the checkpoint the router
selects for that input, not a hand-picked best of three. Jev figures are third-party
published, never measured here (no TypeSafe API access), so sample sizes and prompts differ.
Jev 1.13.0
deepopen (routed)
typed-decisions, 2,000 decisions
0.727
0.766
+0.039
AG News, 4 labels
0.910
0.950
+0.040
DAIR Emotion, 6 labels
0.480
0.595
+0.115
Banking77 (72 vs 77 labels)
0.870
0.425
Jev leads on >20 options
ECE (lower better)
0.246
0.081
3× better (post-temperature)
p50 latency, 1 question
236–276 ms
32.8 ms
7.8× faster
Languages usable
no published benchmark
45 of 51
—
Weights
closed API
Apache 2.0
—
Cost
$0.042 / 1M tokens
$0 self-hosted
—
On DAIR Emotion, Jev assigned zero probability to the true label on 16% of examples — a hard
failure for anything branching on confidence.
Where Jev leads
High-cardinality label spaces (>20 options at default settings): On Banking77, Jev scores 0.870 (on 72 labels) while deepopen scores 0.425 (on 77 labels at default 256-token head budget). This is an architectural token-budget constraint: options share a fixed head_max_len budget (192 tokens on English, 256 on multilingual), so 77 options receive only ~3 to 4 tokens per label, causing text to become indistinguishable. Jev supports up to 255 options out-of-the-box. While deepopen-multilingual supports 1,024 context (and up to 8,192 in the encoder) and you can raise agent.cfg["head_max_len"] = 512 at runtime, Jev is currently better suited for 50+ options in a single prompt without tuning.
Soft distribution matching: On typed-decisions, while deepopen achieves higher argmax accuracy (0.766 vs 0.727), Jev achieves higher soft accuracy (0.580 vs 0.471) against the teacher's full probability distributions.
Out-of-the-box raw calibration: Before temperature scaling, the base checkpoint has higher raw ECE (0.213 vs 0.144). deepopen achieves its 0.081 ECE after domain temperature fitting.
Full detail, including every workflow and all 51 languages: BENCHMARKS.md.
typed-decisions, measured on all three checkpoints
400 cases, 2,000 decisions, four workflows.
model
accuracy
soft acc
Brier
ECE
score MAE
deepopen-typed-decisions
0.766
0.471
0.062
0.213
0.242
deepopen
0.362
0.332
0.316
0.175
0.694
deepopen-multilingual
0.342
0.326
0.439
0.285
0.687
Jev 1.13.0 (published)
0.727
0.580
0.148
0.144
0.391
teacher self-agreement ceiling
0.735
per-question majority class
0.461
random guess
0.318
The fine-tuned checkpoint beats Jev by 3.9 points and clears the teacher ceiling, with 2.4x
better Brier and 1.6x better score MAE. It wins on all four workflows: invoice processing
0.804, security incidents 0.766, customer service 0.764, agent-trace observability 0.730.
By primitive: noul 0.857, choice 0.733, score 0.723.
Two places it still trails Jev: soft accuracy (0.471 vs 0.580 — its argmax is better but
its distributions match the teacher less well) and ECE (0.213 vs 0.144), which temperature
fitting addresses.
The base checkpoints sit below the majority-class baseline (0.362 and 0.342 against 0.461).
All of the capability on this benchmark comes from fine-tuning.
Multilingual (51 languages, MASSIVE intent, 20 options, random = 0.050)
deepopen
deepopen-multilingual
English
0.783
0.657
13 other languages
0.306
0.451
XNLI, English
0.860
0.843
XNLI, 14 other languages
0.521
0.731
Across all 51 languages the English checkpoint macro-averages 0.227 with macro ECE
0.733, and only 23 of 51 languages clear 3x random. Khmer scores 0.000 at 95.2%
confidence. This is why Router exists: the
model's own confidence gives no warning, so the routing decision has to be made before the
forward pass.
English tasks
task
deepopen
deepopen-multilingual
note
AG News
0.947
0.937
in training mix
BoolQ
0.830
0.787
in training mix
DAIR Emotion
0.573
0.513
held out
prompt-injections
0.698
0.578
held out, n=116
SST-5 (ordinal)
0.372
0.282
held out
Calibration
Both checkpoints are over-confident as shipped. Refitting one temperature per (question type,
option count) on held-out data moves mean ECE 0.466 -> 0.081 (deepopen) and
0.314 -> 0.106 (deepopen-multilingual). deepopen-multilingual ships with no fitted
temperatures at all, so fit them before relying on its probabilities.
Honest limits
The base checkpoints are near chance on typed-decisions zero-shot -- 0.362 and 0.352
against a 0.318 random baseline and a 0.461 majority-class baseline. The 0.766 figure comes
from the checkpoint fine-tuned on that benchmark's own training split. deepopen is a fast base to
specialise, not a zero-shot decision engine.
High-cardinality choice questions and token budgets: Sequences split into an option prompt budget (head_max_len) and the remaining document/state budget (max_len - head_max_len):
deepopen (English) defaults to 512 context (head_max_len = 192, ~320 tokens for state).
deepopen-multilingual and deepopen-typed-decisions default to 1,024 context (head_max_len = 256, ~768 tokens for state; mmBERT-base encoder supports up to 8,192 with RoPE).
At default settings, a 77-option question like Banking77 allocates only (256 - 16) // 77 ≈ 3–4 tokens per label, which causes accuracy to fall off sharply (0.425 vs Jev's 0.870). If evaluating 50+ options in a single question:
Raise agent.cfg["head_max_len"] = 512 and agent.cfg["max_len"] = 1024 (or up to 2048 / 4096 / 8192) so every option has enough tokens to remain distinct.
Or split large option sets into a two-step coarse-to-fine hierarchical choice.
Ordinal score questions are the weakest primitive (SST-5 0.372).
deepopen collapses outside English; deepopen-multilingual is weaker on English. Route, or pick
deliberately.
Fine-tune deepopen on your own domain data. The notebook runs on Kaggle's free 2xT4 GPUs and does
the whole loop: build the dataset, train with RLCD (proper-scoring-rule rewards, GRPO-style
policy gradient), fit calibration temperatures, evaluate, and push the result to the Hub.
Fine-tuning is where most of the value is. On the typed-decisions benchmark the base
checkpoints score near chance zero-shot (0.36 and 0.35 against a 0.318 random baseline),
while the fine-tuned checkpoint reaches 0.766 on the same 2,000 decisions -- above
TypeSafe Jev's published 0.727 and above the 0.735 teacher self-agreement ceiling. Treat deepopen
as a fast base to specialise, not as a zero-shot decision engine.
Runtime on 2xT4 is roughly 4-5 hours for 4 epochs over ~30k questions.
License
Apache 2.0. Developed by Deep Open
deepopen (deepopen-com/deepopen)
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DeepOpen 是一个开源的非自回归「系统 1」决策引擎：它不像 LLM 那样逐个 token 地生成文本，而是在一次前向传播中对文本、邮件、工单或 JSON 做出类型化决策（选择、分数或布尔值），覆盖 100 多种语言，最快只需 33 毫秒。