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Nous Research · Official-source model record

Nous-Capybara-34B

Nous

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 Nous-Capybara-34B

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

Which Nous-Capybara-34B does this page describe?

Nous-Capybara-34B is a model record from Nous Research in the Nous 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 Nous-Capybara-34B?

The catalog documents Open weights access for Nous-Capybara-34B. 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 are Nous-Capybara-34B's context and output limits?

The publisher reports the context window as 200K tokens. Source We retain the publisher's notation and qualifiers; no exact token count or hosted serving allowance is inferred from it.

A separate maximum-output value is not established in this record. The context or input figure should not be used as an output allowance.

Context, input and output limits describe different constraints. Do not add the figures together to infer a larger window. Check the endpoint's accounting for conversation history, system instructions, tools, media and generated tokens; a documented maximum is not a guarantee of answer quality or a measured usable capacity.

What can Nous-Capybara-34B 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 Nous-Capybara-34B?

The recorded license label is mit. 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 Nous-Capybara-34B?

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

Training data
Fine-tuning uses approximately 20,000 curated examples, largely synthesized conversations. Source
Base and context
Fine-tuned from Yi 34B for three epochs on Capybara data, with the publisher’s 200K context window. Source
Conversation emphasis
More than 60% of the fine-tuning examples are multi-turn conversations. Source

What is documented about the Nous-Capybara-34B checkpoint?

This evidence refers to NousResearch/Nous-Capybara-34B at recorded revision 6beb706364038ba43350d85734975377236e9546, checked October 23, 2026. Repository metadata

Recorded architecture class: LlamaForCausalLM. 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: eng. 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
200,000
torch_dtype
bfloat16
num_attention_heads
56
num_key_value_heads
8
num_hidden_layers
60
hidden_size
7,168

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 Nous models from Nous Research.

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

Nous-Puffin-70B

Open weights · 4,096 context tokens

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
NousSource ↗
Version
Not documented
Availability
Listed by publisherSource ↗
Access
Open weightsSource ↗
Inputs
TextSource ↗
Outputs
TextSource ↗
Context window
200K tokensSource ↗
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
mitSource ↗
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: LlamaForCausalLM. Repository library label: transformers. Model-card language labels: eng; these labels are not evaluated proficiency scores. Published configuration: max_position_embeddings=200000; dtype=bfloat16; 60 hidden layers. Checkpoint configuration is not a verified hosted serving limit; context and output limits remain separate documented fields.

Model identifiers and aliases 1
  • NousResearch/Nous-Capybara-34B

Follow the evidence

Official sources.

  1. Nous-Capybara-34B — official model card ↗huggingface.co
  2. Nous-Capybara-34B: official publisher repository metadata ↗huggingface.co
  3. Nous-Capybara-34B: publisher model card at pinned revision ↗huggingface.co
  4. Nous-Capybara-34B: published checkpoint configuration at revision 6beb70636403 ↗huggingface.co

From the Headless Thrones newsroom

Editorial about Nous-Capybara-34B

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

Your starting point

Nous-Capybara-34B

Nous Research · Open weights

Browse all models ↗
Inputs
TextSource ↗
Outputs
TextSource ↗
Context window
200K tokensSource ↗
Knowledge cutoff
Not documented

A place to begin comparing

1,313 models to consider.

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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.

View the full coverage report and discovery sources ↗