Open Model WeightsTENCENT WECHAT VISION · FIELD VERIFIED
Primary sources last checked on 26 Oct 2026. Verification freshness is tracked separately from factual change, and unknown values remain explicit rather than inferred.
MODEL INTELLIGENCE · SOURCE-FIRST
Open Model Weights records WeVisOne-VL 4B as a 4.83058B-parameter image and text to boxes masks ocr and alpha mattes open-weight model in the WeVisOne-VL family, with Tencent WeChat Vision listed as the developer. The checked source repository provides 2 recognized weight artifacts, format evidence for PyTorch, Safetensors and a verified context window of 262,144 tokens. Values that are not evidenced in the checked sources remain explicitly undisclosed or unknown.
The repository declares apache-2.0. Commercial use allowed under apache-2.0 conditions. Open Model Weights reports the checked license evidence and classification; this is not legal advice.
No base model is declared in the checked standard lineage metadata, so Open Model Weights does not infer one. Training recipe status: not-disclosed-in-checked-repository; data disclosure status: not-disclosed-in-standard-metadata-or-obvious-model-card-section.
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DIRECT ANSWER · FEATURED MODEL BRIEFING
Source-grounded editorial layer · reviewed 2026-10-26
Tencent WeVisOne-VL 4B is a first-party open-weight visual grounding and alpha-matting VLM released for object detection, visual grounding, OCR localization and pixel-level matting. The publisher documents The original repository represents a higher-capacity counterpart to WeVisOne-VL 2B with roughly 5B reported tensor parameters. This specific version must pass source-verified model-file, model-card, exact SHA and license checks before Open Model Weights publishes a field-verified profile. Its declared 4B-class (about 5B source tensor parameters) and image tasks; text context only if config supported are differentiated from measured tensors, runtime memory and independent performance. Vendor benchmark claims do not confer an automatic Leading designation. Open Model Weights independently checked the source revision, files and declared license for this canonical record; it has not executed model inference.
WHY IT MATTERS
The original repository represents a higher-capacity counterpart to WeVisOne-VL 2B with roughly 5B reported tensor parameters.
The model yields structured points and boxes from image-and-text tasks and supports referring expression localization.
The 4B release includes mask_decoder.pt with 9,609,764 publisher-declared decoder parameters and multistage alpha reconstruction.
MODEL-SELECTION VIEW
The original repository represents a higher-capacity counterpart to WeVisOne-VL 2B with roughly 5B reported tensor parameters. The source-of-truth is the original publisher checkpoint and its revision-pinned file manifest. Keep the technical observation separate from release marketing, hosted-service features, third-party comparisons and any later edits to the model card; this is a prerequisite for accurate open-weight AI discovery.
The 4B product label should not be silently substituted for the exact source-reported tensor total or a memory measurement. A reproducible evaluation must retain the original runtime version, model and dependent component revisions, task prompts, negative examples, latency and actual peak memory. This page documents a practical verification protocol, not an Open Model Weights independent inference result or universal model ranking.
The model yields structured points and boxes from image-and-text tasks and supports referring expression localization. The source-of-truth is the original publisher checkpoint and its revision-pinned file manifest. Keep the technical observation separate from release marketing, hosted-service features, third-party comparisons and any later edits to the model card; this is a prerequisite for accurate open-weight AI discovery.
Evaluate absent target queries and normalization errors, not merely the plausibility of textual outputs. A reproducible evaluation must retain the original runtime version, model and dependent component revisions, task prompts, negative examples, latency and actual peak memory. This page documents a practical verification protocol, not an Open Model Weights independent inference result or universal model ranking.
The 4B release includes mask_decoder.pt with 9,609,764 publisher-declared decoder parameters and multistage alpha reconstruction. The source-of-truth is the original publisher checkpoint and its revision-pinned file manifest. Keep the technical observation separate from release marketing, hosted-service features, third-party comparisons and any later edits to the model card; this is a prerequisite for accurate open-weight AI discovery.
Dense continuous mattes depend on the companion decoder; backbone-only weights are not equivalent to a complete matting deployment. A reproducible evaluation must retain the original runtime version, model and dependent component revisions, task prompts, negative examples, latency and actual peak memory. This page documents a practical verification protocol, not an Open Model Weights independent inference result or universal model ranking.
Tencent reports comparisons on LVIS, Dense200, RefCOCOg, TotalText, ReasonSeg and matting evaluation suites. The source-of-truth is the original publisher checkpoint and its revision-pinned file manifest. Keep the technical observation separate from release marketing, hosted-service features, third-party comparisons and any later edits to the model card; this is a prerequisite for accurate open-weight AI discovery.
The results are publisher-provided; independent external benchmark replication is needed before broad Leading claims. A reproducible evaluation must retain the original runtime version, model and dependent component revisions, task prompts, negative examples, latency and actual peak memory. This page documents a practical verification protocol, not an Open Model Weights independent inference result or universal model ranking.
The repository lists Apache-2.0 and flags research visual-perception tasks with constraints around identification and high-impact decisions. The source-of-truth is the original publisher checkpoint and its revision-pinned file manifest. Keep the technical observation separate from release marketing, hosted-service features, third-party comparisons and any later edits to the model card; this is a prerequisite for accurate open-weight AI discovery.
Third-party components keep original notices, and a license declaration does not independently establish safety in sensitive workflows. A reproducible evaluation must retain the original runtime version, model and dependent component revisions, task prompts, negative examples, latency and actual peak memory. This page documents a practical verification protocol, not an Open Model Weights independent inference result or universal model ranking.
The publisher supplies a Python inference package with image inputs, task selectors and decoder integration. The source-of-truth is the original publisher checkpoint and its revision-pinned file manifest. Keep the technical observation separate from release marketing, hosted-service features, third-party comparisons and any later edits to the model card; this is a prerequisite for accurate open-weight AI discovery.
Benchmark actual end-to-end pipeline memory, pixel boundary quality and latency using a fixed dataset and the correct 4B decoder. A reproducible evaluation must retain the original runtime version, model and dependent component revisions, task prompts, negative examples, latency and actual peak memory. This page documents a practical verification protocol, not an Open Model Weights independent inference result or universal model ranking.
IMPLEMENTATION & EVALUATION
Four-step original-source test plan for Tencent WeVisOne-VL 4B, not an independently executed performance benchmark.
Record exact source SHA, original artifacts and Apache-2.0 license text with upstream notices.
Immutable manifest with official revision and compatible dependency list
Upstream repository can change after intake
Run Tencent WeVisOne-VL 4B through its original official task interface, retaining model and dependency versions.
Repeatable source-aligned output type and stable input handling
Generic family examples may not match this checkpoint
Score task-specific positive and negative cases for object detection, visual grounding, OCR localization and pixel-level matting, with published failure examples.
Held-out task quality table with traceable observed errors
Publisher benchmark numbers are not independent results
Record throughput, p95 latency, peak memory, operating system and rollback criteria under fixed tasks.
Written deployment decision with real observed resource metrics
Weight-only arithmetic omits cache, activations and extra decoder assets
Proposed tests, not OMW benchmark results. Read the official model card at the pinned source revision ↗ Inspect OMW field evidence ↗
PRIMARY & INDEPENDENT SOURCES
FAQ
Tencent WeVisOne-VL 4B is a publisher-released visual grounding and alpha-matting VLM intended for object detection, visual grounding, OCR localization and pixel-level matting. Its official documentation describes The original repository represents a higher-capacity counterpart to WeVisOne-VL 2B with roughly 5B reported tensor parameters. Open Model Weights verifies source records rather than inventing comparative scores.
The official publisher maintains a Hugging Face checkpoint with a model card and weight-file inventory. Its exact source SHA and artifact accessibility must pass a targeted verifier before a canonical record can be called field-verified.
The official repository declares Apache-2.0, but the exact revision's license file, dependency scope and downstream obligations must be checked. The public-weight label does not grant blanket permission for any conceivable deployment.
Notable reflects source-released specialist significance, not an absolute AI leaderboard result. Independent checkpoint-equivalent benchmark results are not currently sufficient to award Leading status for this record.
There is no universally verified device-memory minimum for this deployment. Stored parameters, precision, caches, external decoders, tensor parallelism and inference frameworks all affect the actual peak resident footprint.
Pin a 40-character source SHA and matching model and component files, reproduce the official task interface and test against held-out positive and negative cases. Record real device memory, latency, quality failures and license conditions.
03 · PRACTICAL TECHNICAL PROFILE
Inspect weight-memory estimates, model lineage and runtime evidence separately from promotional or untested performance claims.
DEPLOYMENT PROFILE
These bars visualize weight-only decimal-GB estimates. They exclude KV cache, activations, runtime overhead, optimizer state and sharding.
LICENSE & USE
Commercial use allowed under apache-2.0 conditions
This section summarizes the checked repository evidence. It is not legal advice. Inspect license evidence ↗
LINEAGE & PROVENANCE
Open Model Weights does not infer a hidden base model or training corpus when the checked sources do not declare one.
RUNTIME SIGNALS
Declared and model-card-mentioned runtimes are discovery signals. They are not treated as Open Model Weights runtime tests unless explicitly measured.
EVIDENCE TRAIL
This is where the article remains auditable: source links on one side, the registry's observed change history on the other.
Checked Hugging Face API metadata, exact repository file list, model card and config when accessible.
04 · EVIDENCE AND UPDATES
Inspect dated source checks and any differences between published claims and the evidence; these checks are not independent model benchmarks.
SOURCE-BASED VISUAL BRIEF · NOTABLE
Three evidence-linked visual summaries: parameter scale, illustrative precision memory and verifiable provenance. No performance ranking is implied.
Active parameter count not established.
Repository-derived or publisher-described parameter countFRESHNESS & VERIFICATION
A verification check does not pretend the model changed. Open Model Weights updates the verification date after a real primary-source check, while factual-update dates move only when retained source evidence changes.
CONFLICT INTELLIGENCE
05 · SOURCE RECORD
Full repository-level attributes, licensing, format details and provenance are available below without hiding or rewriting the original data.
01 · WEIGHTS
02 · LICENSE
Classification describes the checked license terms; it is not legal advice.
License / model-card evidence ↗03 · HARDWARE
Weight-only decimal-GB estimate from total parameter count; excludes KV cache, activations, runtime overhead, optimizer state and sharding.
04 · FORMATS
“Not observed” means no matching repository artifact/name/dtype signal was found; it does not prove no third-party conversion exists.
05 · LINEAGE
06 · TRAINING ASSETS
07 · RUNTIME SUPPORT
Open Model Weights separates model-repository evidence from external runtime-release evidence. An external support statement does not change the official model repository's weight formats and does not imply an Open Model Weights inference benchmark.
08 · SOURCES
09 · VERIFICATION HISTORY
Checked Hugging Face API metadata, exact repository file list, model card and config when accessible.
MODELS FOR AI AGENTS · ROLE EVIDENCE
Visual grounding and image-matting component. A supporting component. This checkpoint does not, by category alone, serve as the planning and tool-execution controller.
The curated notable tier is not an agent-readiness rating. Model-specific tool-call success, long-horizon task accuracy and compatible runtime configurations are not independently established here.
Source-linked license: apache-2.0 (allowed commercial-use classification; see original terms).
Context: 262144 configured tokens.
BF16 weights: 9.66 estimated decimal GB, not runtime VRAM.
Compare agent model candidates → · Agent evaluation method → · Inspect original model repository ↗
These records are separately sourced. They do not replace independent model-plus-runtime tests.
No model-specific agent capability evaluation is recorded for this checkpoint. Missing evidence is unknown, not a failed task.
Read evidence details and benchmarks → · Use the agent model selector →