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Nous Research · Official-source model record

OLMo-Bitnet-1B

OLMo

Listed by publisherChecked 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 OLMo-Bitnet-1B

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

Which OLMo-Bitnet-1B does this page describe?

OLMo-Bitnet-1B is a model record from Nous Research in the OLMo family. Source

A separate version identifier is not documented in this record; the model name alone does not establish which snapshot a service runs.

How can you access OLMo-Bitnet-1B?

The catalog documents Open weights access for OLMo-Bitnet-1B. Source

As of the catalog check, this version was listed without confirmed general availability. 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 OLMo-Bitnet-1B 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 OLMo-Bitnet-1B?

The recorded license label is apache-2.0. 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 OLMo-Bitnet-1B?

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

Nominal architecture
One-billion-parameter experimental model trained using the 1.58-bit LLM method. Source
Training and intended use
Research proof of concept trained on the first 60 billion tokens of Dolma. Source
Controlled research comparison
The authors also ran an FP16-weight control with the same hyperparameters to compare against the experimental low-bit method. Source
Inference integration
The supplied inference example installs ai2-olmo and loads the checkpoint through Transformers with its custom model code enabled. Source

What is documented about the OLMo-Bitnet-1B checkpoint?

This evidence refers to NousResearch/OLMo-Bitnet-1B at recorded revision cd17870d2c3a3557b5ca8efc693117c8ea0312f3, checked October 23, 2026. Repository metadata

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

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

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
Nous ResearchSource ↗
Family
OLMoSource ↗
Version
Not documented
Availability
Listed by publisherSource ↗
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
apache-2.0Source ↗
Input price / 1M tokens
Not documented
Output price / 1M tokens
Not documented

Read this with the numbers.

Official publisher model repository; listed status confirms a catalog entry, not a verified hosted API service. Publisher repository evidence (2026-10-23): Publisher task label: text-generation. Published architecture metadata: OLMoModelForCausalLM. Repository library label: transformers.

Model identifiers and aliases 1
  • NousResearch/OLMo-Bitnet-1B

Follow the evidence

Official sources.

  1. OLMo-Bitnet-1B — official model card ↗huggingface.co
  2. OLMo-Bitnet-1B: official publisher repository metadata ↗huggingface.co
  3. OLMo-Bitnet-1B: publisher model card at pinned revision ↗huggingface.co

From the Headless Thrones newsroom

Editorial about OLMo-Bitnet-1B

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

Your starting point

OLMo-Bitnet-1B

Nous Research · 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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