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Tencent · Official-source model record

POINTS-Seeker

POINTS · Version POINTS-Seeker

AvailableChecked Oct 23, 2026
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Publisher-sourced inventory. Specifications come from official documentation and model cards. “Not documented” means we have no verified value. This inventory is separate from our editorial reviews.

Coverage & sources ↗

Current catalog answers

What to know about POINTS-Seeker

AI-compiled from linked model documentation. This is a catalog reference, not an independent performance review. Catalog checked October 23, 2026.

Which POINTS-Seeker does this page describe?

POINTS-Seeker is a model record from Tencent in the POINTS family. Source

The recorded model identifier or checkpoint is POINTS-Seeker. Use that exact identifier when checking documentation or a provider listing. Source

How can you access POINTS-Seeker?

The catalog documents Open weights access for POINTS-Seeker. Source

As of the catalog check, this version was listed as available. Source

Open weights means the model weights are distributed for use under their stated terms. It does not establish a hosted API, an open-source license, or a hardware requirement.

What can POINTS-Seeker receive and generate?

Documented inputs: text. Source

Documented outputs: text. Source

Input support and output generation are separate capabilities. Reading an image does not establish image generation; a text output listing does not establish speech output. File sizes, formats and media limits depend on the documented implementation.

What license is recorded for POINTS-Seeker?

The recorded license label is license-term-of-points-seeker. Source

Read the linked license and associated use terms for this exact version before redistribution, commercial deployment or fine-tuning. A label alone does not establish every permission, restriction or obligation, and it does not determine the terms of a separate hosted service.

What is documented about the POINTS-Seeker checkpoint?

This evidence refers to tencent/POINTS-Seeker at recorded revision 5f75451978c6ee0c139aadcd127b1a9aabc7b7b7, checked October 23, 2026. Repository metadata

Safetensors metadata records 8,913,692,160 stored parameters. This is a stored-weight count, not necessarily the number activated per token, a download size or a GPU-memory requirement. Repository metadata

Recorded architecture class: POINTS_SeekerModel. Repository metadata

Repository task tag: text-generation. A task tag identifies a distribution category; it does not measure performance on that task. Repository metadata

Listed library: transformers. Check that library's version and model support before loading the weights. Repository metadata

Repository language labels: en and zh. These labels are not independent evaluations of language quality. Repository metadata

Repository access gate: false. This records the repository's gate setting when checked; licensing and use terms still apply. Repository metadata

What the configuration file says

The pinned config.json section llm_config contains these values. Pinned configuration

max_position_embeddings
32,768
sliding_window
4,096
torch_dtype
bfloat16
num_attention_heads
32
num_key_value_heads
8
num_hidden_layers
36
hidden_size
4,096

Configuration values describe the checkpoint and its implementation. In particular, max_position_embeddings, sliding_window and rope_scaling do not independently establish an API context limit, tested usable length or hardware requirement. Runtime, quantization, cache, batch size and input length affect deployment behavior.

Read the retrieved model card for setup and qualifications. Model card

Keep the exact version in view

Other POINTS models from Tencent.

These are separate records in the same publisher family. A shared family name does not establish equal capabilities or interchangeable licenses.

The documented record

Specifications, with their sources.

A missing value remains unknown. Input limits, output limits and the total context window are separate measurements.

Publisher
TencentSource ↗
Family
POINTSSource ↗
Version
POINTS-SeekerSource ↗
Availability
AvailableSource ↗
Access
Open weightsSource ↗
Inputs
TextSource ↗
Outputs
TextSource ↗
Context window
Not documented
Maximum input
Not documented
Maximum output
Not documented
Knowledge cutoff
Not documented
Training-data cutoff
Not documented
Training completed
Not documented
Release date
Not documented
License
license-term-of-points-seekerSource ↗
Input price / 1M tokens
Not documented
Output price / 1M tokens
Not documented

Read this with the numbers.

Official publisher weight listing; availability refers to this distribution, not a guaranteed hosted API. Base, SFT and research variants are identified by their exact published names. Publisher repository evidence (2026-10-23): Publisher task label: text-generation. Published architecture metadata: POINTS_SeekerModel. Repository library label: transformers. Model-card language labels: en, zh; these labels are not evaluated proficiency scores. Published safetensors metadata counts 8,913,692,160 stored parameters; this is not an active-parameter count for mixture-of-experts models. Published llm_config configuration: max_position_embeddings=32768; sliding_window=4096; dtype=bfloat16; 36 hidden layers. Checkpoint configuration is not a verified hosted serving limit; context and output limits remain separate documented fields.

Model identifiers and aliases 1
  • tencent/POINTS-Seeker

Follow the evidence

Official sources.

  1. POINTS-Seeker official model card ↗huggingface.co
  2. POINTS-Seeker: official publisher repository metadata ↗huggingface.co
  3. POINTS-Seeker: publisher model card at revision 5f75451978c6 ↗huggingface.co
  4. POINTS-Seeker: published checkpoint configuration at revision 5f75451978c6 ↗huggingface.co

From the Headless Thrones newsroom

Editorial about POINTS-Seeker

No published articles are linked to this exact model yet. New coverage will appear here as it is published.

Your starting point

POINTS-Seeker

Tencent · Open weights

Browse all models ↗
Inputs
TextSource ↗
Outputs
TextSource ↗
Context window
Not documented
Knowledge cutoff
Not documented

A place to begin comparing

1,313 models to consider.

Matches must share an access route, support every documented input type of your starting model, and return text. When both publish a context limit in the same field, the candidate must fall between half and twice that limit. Unknown or differently defined limits remain as broader matches, after verified near-context matches. Within each group, recent documented releases appear first. These criteria do not establish equal quality. The full current inventory is considered.

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Text input; text output · Shared access: Open weights · Capacity match not established

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Text input; text output · Shared access: Open weights · Capacity match not established

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Nemotron-3.5-Parse

Text input; text output · Shared access: Open weights · Capacity match not established

Reflection

Beam 1.0 501B A23B

Text input; text output · Shared access: Open weights · Capacity match not established

IBM

granite-switch-4.1-30b

Text input; text output · Shared access: Open weights · Capacity match not established

IBM

granite-switch-4.1-8b

Text input; text output · Shared access: Open weights · Capacity match not established

Microsoft

MAI-Code-1.1-Flash

Text input; text output · Shared access: Open weights · Capacity match not established

Mistral AI

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Text input; text output · Shared access: Open weights · Capacity match not established

A record you can follow

The source belongs beside the fact.

Official sources

Model records cite the developer’s documentation, release notes and model cards. A listing alone is not a tested service or a recommendation.

Dates mean different things

Knowledge cutoff, training-data cutoff, training completion and release date are separate fields. A model’s name or repository upload date does not establish any of them.

Limits need context

Some publishers use rounded K/M token figures. Deployment, reasoning settings and regional access can change usable limits. Read the source before choosing.

Coverage and last check

Independent editorial model library assembled from primary vendor documentation and official publisher model cards. This is a dated, auditable inventory of the sources reviewed, not a claim to every model worldwide. First-party language-model inventory for OpenAI, Anthropic, Google Gemini/Gemma, xAI, Meta and Reflection. Includes vendor-listed legacy/retired identities with explicit statuses; official pretrained and instruction-tuned checkpoints remain distinct. Excludes embeddings, moderation/guard models, image/audio/video-only generation, community fine-tunes, speculative draft helpers, format/runtime/quantization duplicates, and PaliGemma dataset-specific benchmark fine-tunes. Availability means documented vendor API or downloadable official weights, not guaranteed account eligibility. Unknown specifications and dates remain null. Rounded K/M limits preserve vendor granularity; exact integers are used only where documented. No OpenRouter facts are used. Primary-source inventory of language-capable models from Alibaba/Qwen, DeepSeek, Moonshot, MiniMax, Z.ai, ByteDance Seed, Tencent, Baidu, Xiaomi, StepFun, 01.AI, Shanghai AI Laboratory, Baichuan and OpenBMB. Includes full reachable official public weight inventories plus documented hosted API models and explicit retirements. Embeddings, reward/classifier/router/tokenizer/draft-head components, standalone OCR/document converters and image/video/audio-only generators are excluded; multimodal language models retained. Official packaging variants are folded into aliases where a canonical listing was identifiable; remaining research/base/SFT variants retain exact identities. Not a guarantee every historical/private/regional model is available. Unknown specifications remain null; K/M API display quantities use decimal normalization where explicitly noted, rather than implying verified exact serving ceilings. Model-name dates and repository creation timestamps are never used as release/training dates. Open weights does not mean unrestricted or OSI-open-source licensing. Some model-card bodies were unreachable; their public inventory metadata is retained and disclosed. Official Mistral, Cohere, AI21, NVIDIA Nemotron, Microsoft Phi/MAI, Amazon Nova, IBM Granite, AllenAI OLMo/Molmo, TII Falcon, Nous Research, Arcee and Essential AI model inventory checked 2026-10-23. Includes base, instruction, reasoning, coding and text-producing multimodal models, including legacy API versions. Excludes embeddings/rerankers/reward and safety classifiers, image/audio/video-only generation, robotics-only models, duplicate quantization/runtime packages, adapters, intermediate checkpoint experiments, and third-party mirrors. Arcee coverage focuses on its named product families rather than exploratory merge repositories. Repository listing means listed, not independently verified API availability. K/M figures are nominal decimal normalizations when the source supplies shorthand; deployment-specific exact token limits may differ. Unverified values remain null. This is a source-backed inventory, not a complete claim-level independent editorial review. Supplemental official Liquid AI, Reka and Inception generative language catalog. Excludes media-only generators and decision/classification-only models. No assumed training dates.

Catalog edition updated . Source links are provided on each model record.

View the full coverage report and discovery sources ↗