Update model card: precise RL terms; independent repos

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---
license: other
license_name: lfm-open-license-v1.0
license_link: https://huggingface.co/LiquidAI/LFM2.5-1.2B-Base/blob/main/LICENSE
base_model: LiquidAI/LFM2.5-1.2B-Base
datasets:
  - goutam/rlcd-decision-atlas
language:
  - en
library_name: transformers
pipeline_tag: text-classification
tags:
  - calibration
  - typed-decisions
  - system-one
  - rlcd
  - decision-making
  - lfm2
  - jev-compatible
---
# LFM2.5-1.2B-RLCD (Certa)

A **System-One calibrated decision model**: given a `state` and typed `questions`, it returns
**typed answers with calibrated probabilities** in a single forward pass — no text generation,
no parsing. Runs with the open-source [`certa`](https://github.com/Kurumella/certa) library
(`pip install certa`); training recipe summarized below.

- **Base:** `LiquidAI/LFM2.5-1.2B-Base`
- **Task:** Choice (pick one of ≤26 options), Score (ordered level), Noul (yes/no probability)
- **In-distribution test:** 0.766 accuracy · 0.319 Brier · 0.043 ECE · 0.079 AURC
- **Latency:** ≈ 14 ms per multi-question decision (warm, L40S)
- **API:** compatible with [TypeSafe.ai's Jev](https://docs.typesafe.ai/) `POST /v1/systemone`

## Usage

```bash
pip install certa
```

```python
from certa.engine import ModelEngine
from certa.client import choice, score, verify
engine = ModelEngine("goutam/LFM2.5-1.2B-RLCD")
resp = engine.decide(
    "I was double-charged and support has ignored me for three days.",
    {
        "team":   choice("Route this ticket", {"billing": "charges", "technical": "bugs", "sales": "pricing"}),
        "anger":  score("How angry is the customer?", ["calm", "annoyed", "furious"]),
        "urgent": verify("Does the message convey urgency?"),
    },
)
print(resp.answers["team"].choice, resp.answers["team"].confidence)   # billing 0.79
print(resp.answers["anger"].score)                                    # 1.44 (0–2)
print(resp.answers["urgent"].noul)                                    # 0.78
```

To serve the Jev-compatible HTTP API:

```bash
CERTA_CHECKPOINT=goutam/LFM2.5-1.2B-RLCD pip install 'certa[serve]' && python -m certa --port 8000
```

## Training

1. **Supervised warm start** on 12k labelled rows from
   [`goutam/rlcd-decision-atlas`](https://huggingface.co/datasets/goutam/rlcd-decision-atlas):
   focal (γ=2) + multiclass Brier over the option-token logits.
2. **RLCD refinement** (Reinforcement Learning for Calibrated Decisions) on the remaining ~519k
   rows: **REINFORCE** in a single-step bandit setup (sample one option, one scalar reward),
   reward `r = correct − p(action)` (RPS partial-credit for ordinal questions), **RLOO
   (leave-one-out) baseline**, light KL to the supervised reference.

## Evaluation

| Split | Accuracy | Brier ↓ | ECE ↓ | AURC ↓ |
|---|---|---|---|---|
| Test (in-distribution) | 0.766 | 0.319 | 0.043 | 0.079 |
| Validation | 0.772 | 0.333 | 0.054 | — |
| Holdout (unseen sources) | 0.458 | 0.678 | 0.157 | 0.395 |

Multiclass-sum Brier (range 0–2), top-label 15-bin ECE. **Selective prediction:** acting only
when the top probability ≥ 0.9 covers ≈ 39% of cases at ≈ 2.9% error — a calibrated confidence
gate for act-vs-escalate routing.

## Intended use & limitations

**Use for:** fast calibrated routing, triage, classification, and grading over text where you
need a probability you can threshold — and to abstain/escalate on low-confidence cases.

**Limitations:**
- **Out-of-distribution:** weak (~0.46 accuracy) on entirely unseen source types — broaden
  training coverage for new domains, and re-check calibration on yours.
- **≤ 26 options** per Choice (single-letter decode); larger sets are rejected.
- Trained on English-leaning text sources; non-text / non-English inputs are unsupported.

## License

Weights are released under the **LFM Open License v1.0** (inherited from the base model).
Accompanying code (`certa`, `certa-rlcd`) is Apache-2.0. Not affiliated with TypeSafe.ai.

	---
	license: other
	license_name: lfm-open-license-v1.0
	license_link: https://huggingface.co/LiquidAI/LFM2.5-1.2B-Base/blob/main/LICENSE
	base_model: LiquidAI/LFM2.5-1.2B-Base
	datasets:
	- goutam/rlcd-decision-atlas
	language:
	- en
	library_name: transformers
	pipeline_tag: text-classification
	tags:
	- calibration
	- typed-decisions
	- system-one
	- rlcd
	- decision-making
	- lfm2
	- jev-compatible
	---

	# LFM2.5-1.2B-RLCD (Certa)

	A System-One calibrated decision model: given a `state` and typed `questions`, it returns
	typed answers with calibrated probabilities in a single forward pass — no text generation,
	no parsing. Runs with the open-source [`certa`](https://github.com/Kurumella/certa) library
	(`pip install certa`); training recipe summarized below.

	- Base: `LiquidAI/LFM2.5-1.2B-Base`
	- Task: Choice (pick one of ≤26 options), Score (ordered level), Noul (yes/no probability)
	- In-distribution test: 0.766 accuracy · 0.319 Brier · 0.043 ECE · 0.079 AURC
	- Latency: ≈ 14 ms per multi-question decision (warm, L40S)
	- API: compatible with [TypeSafe.ai's Jev](https://docs.typesafe.ai/) `POST /v1/systemone`

	## Usage

	```bash
	pip install certa
	```

	```python
	from certa.engine import ModelEngine
	from certa.client import choice, score, verify

	engine = ModelEngine("goutam/LFM2.5-1.2B-RLCD")

	resp = engine.decide(
	"I was double-charged and support has ignored me for three days.",
	{
	"team": choice("Route this ticket", {"billing": "charges", "technical": "bugs", "sales": "pricing"}),
	"anger": score("How angry is the customer?", ["calm", "annoyed", "furious"]),
	"urgent": verify("Does the message convey urgency?"),
	},
	)
	print(resp.answers["team"].choice, resp.answers["team"].confidence) # billing 0.79
	print(resp.answers["anger"].score) # 1.44 (0–2)
	print(resp.answers["urgent"].noul) # 0.78
	```

	To serve the Jev-compatible HTTP API:

	```bash
	CERTA_CHECKPOINT=goutam/LFM2.5-1.2B-RLCD pip install 'certa[serve]' && python -m certa --port 8000
	```

	## Training

	1. Supervised warm start on 12k labelled rows from
	[`goutam/rlcd-decision-atlas`](https://huggingface.co/datasets/goutam/rlcd-decision-atlas):
	focal (γ=2) + multiclass Brier over the option-token logits.
	2. RLCD refinement (Reinforcement Learning for Calibrated Decisions) on the remaining ~519k
	rows: REINFORCE in a single-step bandit setup (sample one option, one scalar reward),
	reward `r = correct − p(action)` (RPS partial-credit for ordinal questions), **RLOO
	(leave-one-out) baseline**, light KL to the supervised reference.

	## Evaluation

	\| Split \| Accuracy \| Brier ↓ \| ECE ↓ \| AURC ↓ \|
	\|---\|---\|---\|---\|---\|
	\| Test (in-distribution) \| 0.766 \| 0.319 \| 0.043 \| 0.079 \|
	\| Validation \| 0.772 \| 0.333 \| 0.054 \| — \|
	\| Holdout (unseen sources) \| 0.458 \| 0.678 \| 0.157 \| 0.395 \|

	Multiclass-sum Brier (range 0–2), top-label 15-bin ECE. Selective prediction: acting only
	when the top probability ≥ 0.9 covers ≈ 39% of cases at ≈ 2.9% error — a calibrated confidence
	gate for act-vs-escalate routing.

	## Intended use & limitations

	Use for: fast calibrated routing, triage, classification, and grading over text where you
	need a probability you can threshold — and to abstain/escalate on low-confidence cases.

	Limitations:
	- Out-of-distribution: weak (~0.46 accuracy) on entirely unseen source types — broaden
	training coverage for new domains, and re-check calibration on yours.
	- ≤ 26 options per Choice (single-letter decode); larger sets are rejected.
	- Trained on English-leaning text sources; non-text / non-English inputs are unsupported.

	## License

	Weights are released under the LFM Open License v1.0 (inherited from the base model).
	Accompanying code (`certa`, `certa-rlcd`) is Apache-2.0. Not affiliated with TypeSafe.ai.