KDL-Frontier-Parser-nano-v2

A 1.2B-parameter open-weight document parsing model, packaged and orchestrated by KoreaDeep as the nano tier of the KDL Frontier Parser family.

ParseBench results

Measured 2026-10-28 with the official ParseBench harness, full set, single end-to-end pass (2,078 unique documents; 2,553 group test cases; 0 inference failures):

Dimension	Metric	Score
Overall (mean)	mean of 5 dimensions	78.96
Tables	grits_trm_composite	86.06
Visual Grounding / Layout	layout_element_rule_pass_rate	75.31
Content Faithfulness	content_faithfulness	87.64
Semantic Formatting	semantic_formatting	76.91
Charts	rule_pass_rate	68.87

Author-measured with unmodified upstream 7de04fc11c6a692dfc5e1a86c7be817f3f0af8de and dataset revision 2805a1d940f95a203e0ae4b88be9934f7765b3fc. All 2,553 officially eligible group cases were scored successfully; the reports additionally retain 32 skipped records outside those case counts. This is one pass, not a repeated-run mean; maintainer reproduction and official leaderboard acceptance are pending. Full validation summary · Reproduction instructions.

Serving

vllm serve <this-repo> \
  --served-model-name kdl-frontier-parser-nano \
  --max-model-len 8192 \
  --gpu-memory-utilization 0.85 \
  --max-num-seqs 24 \
  --trust-remote-code \
  --limit-mm-per-prompt '{"image":1}'

Usage

This model is not a single-shot end-to-end parser. It runs as a pipeline: detect layout, crop each region, then call the model again per region with a task-specific prompt.

Prompts

Each task uses a fixed prompt (note the leading newline):

Task	Prompt
Layout	\nLayout Detection:
Text	\nText Recognition:
Table	\nTable Recognition:
Formula	\nFormula Recognition:
Figure	\nImage Analysis:

Table recognition returns OTSL. Other output formats are left to the caller.

Inference notes

• Serve with --trust-remote-code and --limit-mm-per-prompt '{"image":1}' (one image per request).
• Set enable_thinking=False in the chat template.
• Pass skip_special_tokens=False when decoding.
• Greedy decoding (temperature=0).

Feed page images, not PDFs. Chat UIs (e.g. open-webui) with free-form prompts will not work — use the prompts above.

Benchmark methodology

The ParseBench score is an end-to-end pipeline measurement — this model served via vLLM plus deterministic rule-based post-processing of model output — consistent with how all ParseBench providers are evaluated (every provider is a submitter-hosted endpoint). No other learned models, classifiers, or ensembles are involved: single model, single pass.

About

Built by KoreaDeep, a document-AI company. The larger KDL-Frontier-Parser-ultra is available through DEEP Agent.