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Shanghai AI Laboratory · Official-source model record

internlm2_5-step-prover

InternLM · Version internlm2_5-step-prover

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 internlm2_5-step-prover

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

Which internlm2_5-step-prover does this page describe?

internlm2_5-step-prover is a model record from Shanghai AI Laboratory in the InternLM family. Source

The recorded model identifier or checkpoint is internlm2_5-step-prover. Use that exact identifier when checking documentation or a provider listing. Source

How can you access internlm2_5-step-prover?

The catalog documents Open weights access for internlm2_5-step-prover. 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 dates are documented for internlm2_5-step-prover?

Release date: October 22, 2024. Source This is a release milestone, not a knowledge or training cutoff. The source labels this “Publisher’s announcement links this exact policy checkpoint.”

Neither a knowledge cutoff nor a training-data cutoff is established here. A release date, repository upload or filename cannot supply those missing dates.

What can internlm2_5-step-prover 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 internlm2_5-step-prover?

The recorded license label is Custom license (see official model card). 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 distinguishes internlm2_5-step-prover?

These details come from the linked publisher material for this model. Reported capabilities and training descriptions are publisher statements, not independent test results.

Specialization
Formal mathematical proving. Source
Nominal parameters
7 billion. Source
Policy checkpoint role
This repository is the proof-generating policy checkpoint; the associated critic is a separately distributed model. Source
Proof-search artifact
The release includes 14k proofs found in Lean-Workbook, according to the publisher’s announcement. Source

What is documented about the internlm2_5-step-prover checkpoint?

This evidence refers to internlm/internlm2_5-step-prover at recorded revision 9cb19fbd40b41a55a7586aed881df435bdef5e9f, checked October 23, 2026. Repository metadata

Recorded architecture class: InternLM2ForCausalLM. Repository metadata

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

Repository language labels: en. 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 contains these values. Pinned configuration

max_position_embeddings
8,192
rope_scaling
{"factor":1,"type":"dynamic"}
torch_dtype
float16
num_attention_heads
32
num_key_value_heads
8
num_hidden_layers
32
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 InternLM models from Shanghai AI Laboratory.

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

internlm-20b

Open weights

internlm-7b

Open weights

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
Shanghai AI LaboratorySource ↗
Family
InternLMSource ↗
Version
internlm2_5-step-proverSource ↗
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
Oct 22, 2024Publisher’s announcement links this exact policy checkpoint · day precisionSource ↗
License
Custom license (see official model card)Source ↗
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: InternLM2ForCausalLM. Model-card language labels: en; these labels are not evaluated proficiency scores. Published configuration: max_position_embeddings=8192; dtype=float16; 32 hidden layers. The configuration includes explicit RoPE scaling ({"factor":1,"type":"dynamic"}); no effective context extension is inferred. Checkpoint configuration is not a verified hosted serving limit; context and output limits remain separate documented fields.

Model identifiers and aliases 1
  • internlm/internlm2_5-step-prover

Follow the evidence

Official sources.

  1. internlm2_5-step-prover official model card ↗huggingface.co
  2. internlm2_5-step-prover: official publisher repository metadata ↗huggingface.co
  3. internlm2_5-step-prover — official model card ↗huggingface.co
  4. internlm2_5-step-prover: published checkpoint configuration at revision 9cb19fbd40b4 ↗huggingface.co
  5. Official checkpoint documentation or implementation ↗raw.githubusercontent.com

From the Headless Thrones newsroom

Editorial about internlm2_5-step-prover

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

Your starting point

internlm2_5-step-prover

Shanghai AI Laboratory · Open weights

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

A place to begin comparing

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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.

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