feat(server): serve GLiNER2.5-Decide typed-decision models by svonava · Pull Request #368 · superlinked/sie

svonava · 2026-09-25T05:39:16Z

Summary

Serves Fastino's GLiNER2.5-Decide classifiers on extract, with a probability for every option. They take the same typed questions as the Laya models, GLiClass-style label groups, or plain labels.

sie_id	Encoder (architecture)	Window	Revision
`fastino/GLiNER2.5-Decide`	DeBERTa-v3-large (span)	512	`7ee5da4c`
`fastino/GLiNER2.5-multi-Decide`	mDeBERTa-v3-base (boundary)	2,048	`6bc1d43d`
`fastino/GLiNER2.5-Decide-1B`	Ettin 1B, ModernBERT (span)	2,048	`52c94d3b`

All three are Apache-2.0, ungated, and loaded through gliner2 2.0.0 AutoExtractor at the pinned revisions, in float16 on GPU (the package's quantize=True). Windows: English Decide uses 512 tokens, as gliner2-large-v1 (its base) does; its DeBERTa-v3 encoder was pretrained on 512 positions and neither the card nor the config gives a longer length. The multilingual model's config sets max_len 4096 words, and the 1B's Ettin encoder has 7999 positions with an 8192-token tokenizer limit, so both use 2,048. The 1B YAML notes that these weights are the step-285000 checkpoint, while the card's benchmark score came from an earlier step-195000 checkpoint.

Request contract

output_schema: Laya's question mapping {qid: {type, instructions, criteria, labels?}}, validated by Laya's own rules.
- choice becomes one label per criterion; a criterion's description becomes the label description.
- noul becomes the labels yes/no, or the question's labels wording, described by criteria.true/criteria.false.
- score becomes the ordinal labels "0"…"k-1", each described by its criterion.
- Answers come back in data[qid] in Laya's shapes. choice/score carry every option's probability and confidence = 1 - H/log k; score adds the expected level and legend. noul carries noul (P(yes)), answer, and confidence = max(p, 1-p).
options.label_groups (with classification_type/multi_label): the group name is the task name, and a request instruction is every group's prompt. Answers are data[group] in GLiClass's shapes ({"type": "choice", ...}, or {"labels", "probabilities"} for multi-label), plus group.label classifications.
labels: one pick-one task (named by options.classification_task, default label), returning every label in classifications. threshold post-filters.
Items: text, or a Laya metadata.state (a string, a JSON object, or a list of turns, of which the newest are kept).

One row per item

The model reads all of a call's questions next to the document in one sequence: (prompts and labels) [SEP_TEXT] document. It scores every label from its [L] marker in the same forward pass, as gliner2.classification.Classifier does. So one forward pass answers all of an item's questions, not one row per question. Because the questions attend to each other, a question's probabilities depend on the other questions sent with it; this is documented. The processor builds the task prompt once per request, and each document is appended the way the processor appends it. group_encoding other than joint is refused.

Cross-label constraints are left as a follow-up. The package's constrained decoder changes the chosen labels, not the probabilities. It would need a request DSL, and its beam-decoder dead-end fix is not in a release yet.

Dependency: gliner2 2.0.0 in the transformers5 bundle

2.0.0 is the only 2.x on PyPI. The later fixes on GitHub main (16 and 21 Sep) touch constrained decoding and boundary record fields; neither path is used here. I ran each model through SIE's adapter on fixed inputs on an L4, before and after the bump:

Model	Stack	Result
gliguard-LLMGuardrails-300M	transformers 5.17, gliner2 1.3.2 → 2.0.0	identical on all cases
gliner2-base-v1 / large-v1	transformers 4.57.6, gliner2 1.3.2 → 2.0.0	classification, relations, single-text NER, and enum extraction identical. Batched long-text NER now returns every repeated mention: 1.3.2 deduplicated spans by lowercase text, 2.0.0 by offsets. Structured extraction is rescored (a "Priya Raman" field's confidence went from 0.53 to 0.90), and some string fields come back `null`, which the adapter then rejects.

So the root lock and the default bundle stay on gliner2 1.x, keeping gliner2-base/large unchanged on every path, including installs that use only the lock. The transformers5 bundle, which already serves GLiGuard, pins gliner2==2.0.0 and lists the new adapter. resolve-deps routes the Decide models there, and the bundle resolves with transformers 5.17.0. The multilingual and 1B checkpoints were saved with transformers 5.17.

RoPE (GLiNER2.5-Decide-1B)

The Ettin encoder declares its RoPE bases only in transformers-5 rope_parameters (160000 for full and sliding-window layers). Transformers 4.57 ignores that field and builds the sliding-window layers with base 10000. On fixed inputs that moved probabilities by up to 0.61, and 6 of 19 top answers changed. The adapter now:

checks the base every loaded rotary table encodes against the checkpoint's declaration, in float32 before any cast, and refuses to load on a mismatch;
under transformers 4, loads through a temporary overlay that writes the bases into global_rope_theta/local_rope_theta and renames the TokenizersBackend tokenizer class, which transformers 4 lacks and which makes AutoExtractor fail outright.

Transformers 5 needs neither step: the loaded bases are 160000/160000. With the overlay on transformers 4.57, the adapter matches transformers 5.17 on CPU (max |Δp| 1.6e-7). A unit test reproduces the trap on a real transformers-4 ModernBERT built from a shrunken copy of the 1B's encoder config. The multilingual encoder is mDeBERTa, which has no RoPE; its probabilities are identical under 4.57 and 5.17, and it loads in float32 in both.

Parity with gliner2 2.0.0

Goldens are the package's own float32 CPU probabilities, from scripts/generate_gliner2_decide_goldens.py, which runs no SIE code. They cover 6 cases × 5 texts: four question types together, a question about a passage, single- and multi-label groups, labels, and a document longer than the window.

Model	CPU fp32 vs golden	CPU, transformers 4.57 path	GPU fp16 vs package on same weights	GPU fp16 vs fp32 golden	Top-1	Rows byte-identical
Decide	4.7e-8	4.7e-8	5.4e-4	1.1e-3	59/59	26/26
multi-Decide	3.9e-8	3.9e-8	2.3e-3	2.3e-3	59/59	26/26
Decide-1B	1.6e-7	1.6e-7	7.6e-4	9.3e-4	59/59	26/26

Values are max |Δp|. The GPU gap to the package comes from float16 rounding: the adapter pads a request's rows into one batch, while the package scores one text at a time.

Speed (L4 GPU with a 1-vCPU container quota: the GPU sandbox cannot be given more CPUs; in-process, ~80-word items)

Model	1 item: 1 q / 4 q in one call / 4 q in 4 calls	64 items: 1 q / 4 q in one call / 4 q in 4 calls	Full-window rows, 4 q
Decide (512-token window)	43.1 / 43.5 / 170 ms	331 / 152 / 84 items/s	30.6 items/s
multi-Decide	22.4 / 22.7 / 88.7 ms	703 / 317 / 182 items/s	6.5 items/s
Decide-1B	25.2 / 26.4 / 104 ms	134 / 76 / 34 items/s	6.0 items/s

Asking four questions in one call costs about the same as one question, and gives 1.7–2.2× the question throughput of four separate calls. Host-side preparation (parsing, prompt, word reads) of 64 items takes about 10 ms on the single vCPU; how much more CPUs would speed up kernel launches was not measured.

Usage and bounds

Metering: usage.input_tokens counts the document tokens read plus the instructions and criteria text, following the Laya and GLiClass policy for free-form instructions and criteria. That text is encoded next to every item at full cost. Question ids, group names, and label names are not counted, and errored items count 0.
Bounds on the request, checked before tokenizing:
- at most 64 questions or groups, 64 options per question, and 1,024 in total;
- names up to 128 characters, labels up to 256, each instruction or description up to 2,048, and 65,536 characters in all;
- strings containing the prompt markers are refused with 400 INVALID_INPUT.
Prompt share of the window: the questions may take at most 512 tokens, or half the window when that is less (256 for English Decide), as with GLiFormer's unbilled prompt. A larger request fails with 400 INPUT_TOO_LONG.
Documents: read lazily up to the whole words that fit in the rest of the window, so a multi-megabyte text is never tokenized whole. A word over 4,096 characters, or text past 64 characters per window token, also ends what is read.
Per-item errors: blank or malformed items fail alone with INVALID_INPUT. An item none of whose words fits, or any item that doesn't fit with overflow_policy="error", fails alone with INPUT_TOO_LONG, and non-finite scores with INFERENCE_ERROR.
Verified end to end through sie-server serve and SIEClient for all three models.

Word splitting in linear time

gliner2's word-splitting regex tries its e-mail alternative at every word. That alternative scans the whole run of e-mail characters ahead before failing, so a run like "...." or "a.a.a." costs time quadratic in its length. With the package's splitter, a 2 MiB item of "." took 15–31 s, and a 64 KiB conversation state 16 s, on the model's inference thread.

words.py is a linear-time equivalent of that splitter. It tries the same alternatives in the same order but scans each run of e-mail characters once.
- It yields exactly the package's words. That is checked against the package regex, verbatim copies of gliner2 1.x's and 2.x's splitters, and the installed package, on a corpus, 20,000 fuzzed texts, and the parity goldens' texts.
- The adapter refuses to load a gliner2 whose splitter it has no equivalent for.
Conversations are read run by run from the newest turn. A run longer than 4,096 characters contributes only its last 4,096 characters, and reading stops there, which counts as not read whole. Reading also stops at 4 words per window token.
The same items now take 20–30 ms, and the conversation state about 5 ms (real processor and tokenizer, encoder stubbed).
The parity goldens and GPU parity still pass, with rows byte-identical.
The same splitter fix for the existing GLiNER2 adapters (gliner2 1.x) is in a separate PR from main, which adds words.py with identical content.

options.overflow_policy is now validated where both the HTTP and the queue paths merge options (core.runtime_options). An invalid value such as [] is now a 400 INVALID_INPUT on the queue path too, instead of an inference_error.

Tests

test_gliner2_decide.py (CI): contract mapping, every malformed-request error, bounds, answer shapes, confidence, metering, truncation, and per-item errors. It also covers states, batching order, cost estimates, the RoPE check and the transformers-4 overlay, a real ModernBERT reproduction of the trap, and the YAML and bundle placement.
test_gliner2_decide_parity.py (model): CPU goldens with row hashes, GPU against the live package, and the 1B's loaded RoPE bases.
test_all_models.py: the Laya typed questions on all three models. Every Decide model answers ('refund', True, 'billing'), as the Laya models do.
Model tests need gliner2 2.x; run them with the transformers5 bundle requirements (see the test module docstrings).

🤖 Generated with Claude Code

Summary by CodeRabbit

New Features
- Added support for standard, multi-task, and 1B GLiNER2.5-Decide models for text classification and extraction.
- Supports typed questions, grouped labels, and flat label lists, with answers that include probabilities and confidence scores.
- Added input-token accounting and per-item errors for requests that exceed supported limits.
Bug Fixes
- Invalid overflow policies are consistently rejected with a clear error.
Documentation
- Added quickstart examples and guidance on supported inputs, token counting, request limits, and how questions in a request affect one another’s probabilities.
- Documented access through the Transformers 5 image or native installation.

Add Fastino's GLiNER2.5-Decide classifiers (fastino/GLiNER2.5-Decide, GLiNER2.5-multi-Decide, GLiNER2.5-Decide-1B) to extract, loaded through gliner2 2.x AutoExtractor at pinned revisions. Every option of every question gets a probability. Requests take the same shapes as the other decision models: Laya's typed question mapping in output_schema (choice, noul as yes/no, score as the ordinal labels "0".."k-1", each described by its criterion), GLiClass's options.label_groups (single- or multi-label), or plain labels. Answers come back in Laya's and GLiClass's shapes, with confidence 1 - H/log k (max(p, 1 - p) for noul). Items are texts or Laya states. Each item is one encoder row holding every question and the document, so one forward pass answers all of a call's questions, as gliner2.classification does; each question's probabilities depend on the others sent with it. The task prompt is built once per request by the package's processor, and documents are read lazily up to the whole words that fit in the window. Usage counts the document tokens read plus instructions and criteria text; question ids and label names are not counted, and errored items count nothing. Questions, options, names, free text, and the prompt's share of the window are bounded before anything is tokenized, and unreadable or oversized items fail alone. gliner2 2.0.0 changes gliner2-base/large-v1 outputs (every repeated NER mention is returned; structured extraction is rescored and can return null fields), so the root lock and default bundle stay on 1.x. The transformers5 bundle, which already serves GLiGuard, pins gliner2 2.0.0; GLiGuard's outputs are identical under it. The 1B's Ettin encoder declares its RoPE bases only in transformers-5 rope_parameters (160000 for all layers); transformers 4 runs its sliding-window layers at 10000 (max |dp| 0.61 on fixed inputs). The adapter checks the bases the loaded encoder uses, and under transformers 4 loads the checkpoint through an overlay that writes them where that version reads them and names a tokenizer class it has. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

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📝 Walkthrough

Walkthrough

Adds GLiNER2.5 Decide support for typed questions, label groups, and flat labels. The change adds an adapter, three model configurations, dependency-bundle settings, usage documentation, and tests. Runtime option merging also validates overflow policies and reports invalid values through the API.

Changes

GLiNER2.5 Decide support

Layer / File(s)	Summary
Request parsing and answer formats `packages/sie_server/src/sie_server/adapters/gliner2/decisions.py`, `packages/sie_server/tests/adapters/test_gliner2_decide.py`	Parses typed questions, label groups, and flat labels. Validates request fields and limits, then converts logits into probabilities, answers, and classifications.
Checkpoint loading and Transformers compatibility `packages/sie_server/src/sie_server/adapters/gliner2/decide.py`, `packages/sie_server/src/sie_server/adapters/gliner2/words.py`, `packages/sie_server/tests/adapters/test_gliner2_decide.py`, `packages/sie_server/tests/adapters/test_gliner2_words.py`	Adds checkpoint loading, Transformers 4 compatibility overlays, ModernBERT RoPE verification, and a linear-time word splitter tested against GLiNER2 splitters.
Request execution and batched scoring `packages/sie_server/src/sie_server/adapters/gliner2/decide.py`, `packages/sie_server/tests/adapters/test_gliner2_decide.py`	Builds prompts and bounded document rows, handles per-item errors and token counts, batches scoring, and returns answers or classifications.
Model catalog, bundles, and usage documentation `packages/sie_server/models/fastino__GLiNER2.5-.yaml`, `packages/sie_server/bundles/.yaml`, `packages/sie_server/pyproject.toml`, `packages/sie_server/tests/adapters/test_gliner2.py`, `README.md`, `packages/sie_sdk/README.md`, `packages/sie_server/README.md`	Adds three model configurations and the Decide adapter to the transformers5 bundle, which pins `gliner2` to `2.0.0`. Documentation covers supported inputs, outputs, token accounting, and limits.
Golden generation and parity coverage `packages/sie_server/scripts/generate_gliner2_decide_goldens.py`, `packages/sie_server/tests/adapters/goldens/gliner2_decide/*`, `packages/sie_server/tests/adapters/test_gliner2_decide_parity.py`, `packages/sie_server/tests/test_all_models.py`	Generates reference rows and probabilities, adds golden fixtures, checks parity and long-document handling, and tests typed-decision outputs for the three models.

Runtime overflow-policy validation

Layer / File(s)	Summary
Merged option validation and API errors `packages/sie_server/src/sie_server/core/runtime_options.py`, `packages/sie_server/src/sie_server/api/options.py`, `packages/sie_server/tests/core/test_runtime_options.py`, `packages/sie_server/tests/api/test_option.py`	Validates merged overflow policies and maps invalid values to the `invalid_overflow_policy` API span attribute. Tests cover invalid and accepted values.

Sequence Diagram(s)

sequenceDiagram
  participant Client
  participant GLiNER2DecideAdapter
  participant parse_request
  participant GLiNER2Processor
  participant Encoder
  participant ClassificationHead
  Client->>GLiNER2DecideAdapter: Submit decision request
  GLiNER2DecideAdapter->>parse_request: Parse tasks and options
  parse_request-->>GLiNER2DecideAdapter: Return task schema and metadata
  GLiNER2DecideAdapter->>GLiNER2Processor: Build prompt and document rows
  GLiNER2Processor-->>GLiNER2DecideAdapter: Return tokenized rows and label positions
  GLiNER2DecideAdapter->>Encoder: Encode batched rows
  Encoder-->>GLiNER2DecideAdapter: Return encoded rows
  GLiNER2DecideAdapter->>ClassificationHead: Score label positions
  ClassificationHead-->>GLiNER2DecideAdapter: Return logits
  GLiNER2DecideAdapter-->>Client: Return answers or classifications and token counts

Suggested reviewers: krisztian-gajdar

Priority: ➖ Normal

Merge Risk: 🔵 Low · up to ec7eb

Questions sent together can change one another’s probabilities and derived scores, so readers may assume an independence that does not hold. Clarify this in the usage guidance; the issue is bounded and straightforward to address.

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 30.43% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 161 functions across 13 files. (2 skipped…	Write docstrings for the functions missing them to satisfy the coverage threshold.

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Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title clearly and concisely identifies the main change: serving GLiNER2.5-Decide typed-decision models in the server.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

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Inline comments:
In `@packages/sie_server/README.md`:
- Around line 72-73: Update the native Transformers 5 installation instructions
in the README to ensure the transformers5 bundle dependencies, including its
gliner2==2.0.0 pin, are installed before serving Decide models; alternatively,
direct Decide users to the transformers5 image. Keep the existing
model-selection guidance aligned with the chosen installation path.

In `@packages/sie_server/src/sie_server/adapters/gliner2/decide.py`:
- Around line 433-435: Update the `word_ids` caching flow so only short words
are cached; tokenize longer words directly without adding them to the LRU cache.
Define a suitable maximum cached-word length alongside `_WORD_CACHE_SIZE`, and
preserve the existing tokenization behavior for both paths.

In `@README.md`:
- Around line 194-195: Update the Quickstart example in README.md so readers
start a transformers5 image or an equivalently provisioned server before calling
client.extract with fastino/GLiNER2.5-Decide; keep the existing example intact.

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README.md
packages/sie_sdk/README.md
packages/sie_server/README.md
packages/sie_server/bundles/default.yaml
packages/sie_server/bundles/transformers5.yaml
packages/sie_server/models/fastino__GLiNER2.5-Decide-1B.yaml
packages/sie_server/models/fastino__GLiNER2.5-Decide.yaml
packages/sie_server/models/fastino__GLiNER2.5-multi-Decide.yaml
packages/sie_server/pyproject.toml
packages/sie_server/scripts/generate_gliner2_decide_goldens.py
packages/sie_server/src/sie_server/adapters/gliner2/decide.py
packages/sie_server/src/sie_server/adapters/gliner2/decisions.py
packages/sie_server/tests/adapters/goldens/gliner2_decide/fastino__GLiNER2.5-Decide-1B.json
packages/sie_server/tests/adapters/goldens/gliner2_decide/fastino__GLiNER2.5-Decide.json
packages/sie_server/tests/adapters/goldens/gliner2_decide/fastino__GLiNER2.5-multi-Decide.json
packages/sie_server/tests/adapters/test_gliner2.py
packages/sie_server/tests/adapters/test_gliner2_decide.py
packages/sie_server/tests/adapters/test_gliner2_decide_parity.py
packages/sie_server/tests/test_all_models.py

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The adapter's word-token cache kept up to 65,536 words of any length across requests, so documents full of long unique words (hashes, base64 runs) could hold gigabytes. Cache only words of at most 32 characters, 16,384 of them, and tokenize longer words each time. The package processor also keeps every task prompt it tokenizes in its own LRU; clear it after the prompt is built so request prompts are not retained. Document that the Decide models are served by the transformers5 image, and how a native install adds gliner2 2.x next to sie-server's own 1.x range. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

…odel Serve fastino/GLiNER2.5-Decide with max_sequence_length 512, as fastino/gliner2-large-v1 is served. Its DeBERTa-v3-large encoder was pretrained on 512 positions, and neither the model card nor the checkpoint config gives a longer length. The questions may then take half the window, 256 tokens. Keep 2048 for the multilingual and 1B models, and note in each config what supports it: the multilingual checkpoint's max_len of 4096 words, and the Ettin encoder's 7999 positions and 8192-token tokenizer limit. Regenerate the English parity golden at the new window; only its long-document case changes. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

gliner2's word-splitting regex tries its e-mail alternative at every word, and that alternative scans the whole run of e-mail characters ahead before failing, so a run such as "...." or "a.a.a." costs time quadratic in its length. Two megabytes of "." took 15 to 31 seconds per item, and a 64 KiB conversation state 16 seconds, on the thread that serves every request. Split words with a linear-time equivalent of that regex, which tries the same alternatives in the same order but scans each run of e-mail characters once; it yields exactly the package's words, checked against the package and verbatim copies of its 1.x and 2.x splitters on a corpus, fuzzed texts and the parity goldens. The adapter refuses to load a gliner2 whose splitter it has no equivalent for. Conversations are read run by run from their newest turn, keeping only the last 4,096 characters of a longer run, and reading also stops at 4 words per window token. Validate options.overflow_policy where both the HTTP and the queue paths merge options, so an invalid value is a 400 on both instead of an inference error on the queue path. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

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Inline comments:
In `@packages/sie_server/src/sie_server/adapters/gliner2/decide.py`:
- Around line 670-671: Update `_read` to mark the list state incomplete when
`_words_from_end` truncates an overlong run, even if the retained tail fits in
the available room. Detect truncation by checking whether the oldest retained
word falls within a run longer than `_MAX_WORD_CHARS`; do not rely on a raw
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packages/sie_server/README.md
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packages/sie_server/src/sie_server/adapters/gliner2/words.py
packages/sie_server/src/sie_server/api/options.py
packages/sie_server/src/sie_server/core/runtime_options.py
packages/sie_server/tests/adapters/test_gliner2_decide.py
packages/sie_server/tests/adapters/test_gliner2_words.py
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packages/sie_server/README.md

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…read whole When a conversation's newest run of non-space characters is longer than the adapter reads, only its tail is read and older turns are skipped. If that tail fit in the window, the item still counted as read whole, so overflow_policy "error" accepted it. Mark the cut so the item is not read whole. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

# Conflicts: # packages/sie_sdk/README.md # packages/sie_server/README.md

svonava · 2026-09-25T10:25:46Z

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Inline comments:
In `@packages/sie_server/README.md`:
- Around line 182-183: Clarify in the README’s GLiNER2.5-Decide encoding
description that questions in one request are not independent: because they
share an encoder context, adding or changing one question can alter another
question’s probabilities and derived score.

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…ndependent Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

svonava · 2026-09-25T10:56:21Z

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gliner2 2.x changes gliner2-base-v1 and gliner2-large-v1 outputs (repeated NER mentions, structured extraction) #369

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fix(server): split GLiNER2 documents in linear time #371

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Merge remote-tracking branch 'origin/main' into gliner25-decide

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# Conflicts: # packages/sie_sdk/README.md # packages/sie_server/README.md

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Comment thread packages/sie_server/README.md Outdated

docs(server): say GLiNER2.5-Decide questions in one request are not i…

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…ndependent Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

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Summary

Request contract

One row per item

Dependency: gliner2 2.0.0 in the transformers5 bundle

RoPE (GLiNER2.5-Decide-1B)

Parity with gliner2 2.0.0

Speed (L4 GPU with a 1-vCPU container quota: the GPU sandbox cannot be given more CPUs; in-process, ~80-word items)

Usage and bounds

Word splitting in linear time

Tests

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Walkthrough

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Sequence Diagram(s)

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