GKA-primed-HQwen3-32B-Reasoner
Amazon Web Serviceshuggingface.co/amazon/GKA-primed-HQwen3-32B-Reas
Updated 7 h ago · first seen 11 Sept 2026
model_01M294X4CS9SP7SAX89Y3FHBK0
Overview
Identity
Identity block not returned by the API for this entity.
Openness
Openness not classified yet — no sourced evidence to place this model in the ontology.
Key facts
- Release date
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium
- Model card
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium
Architecture
- Architecture
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium
- Model type
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium
- Parameters
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium
- Weights dtype
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium
- File size
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium
- Library name
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium
- Pipeline tag
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium
- Hugging Face repo
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 12 h agomedium
Hardware fit37
Assumptions (6)
- Estimated, not measured: weights = parameters × bytes/param × 1.15 runtime overhead.
- bytes/param: 4bit = 0.5, 8bit = 1.0, fp16 = 2.0 (uniform quantization, no per-layer exceptions).
- KV cache approximated at 0.5 GB per 8 192 tokens of context, independent of architecture (GQA/MLA models need less).
- A model 'fits' when the estimate is at most the device memory minus 2 GB reserved for the OS and framework.
- Mixture-of-experts models are estimated on total parameters (all experts must be resident); active parameters are ignored.
- Device memory uses the largest configuration when several are listed (e.g. Apple silicon tiers).
Lineage
Open in Graph →- ancestor: Qwen3 32B
Papers1
- arXiv:2511.21016Active35
Timeline1
Full timeline →New model: GKA-primed-HQwen3-32B-Reasoner (Amazon Web Services)
huggingface
Change history19
Viewing AI Atlas as of 12 Sept 2026 — attributes exactly as the atlas knew them on that day; later corrections are not shown.
Back to today →Attributes as of 12 Sept 2026 23 claims in force
- Release date
- 31 Mar 2026
- Openness
- open-weights
- License
- Apache-2.0
- Architecture
- HybridQwen3ForCausalLM
- Parameters
- 34.1B
- Base model
- Qwen/Qwen3-32B
- File size
- 68.3 GB
- Hugging Face repo
- amazon/GKA-primed-HQwen3-32B-Reasoner
- Pipeline tag
- text-generation
- Downloads
- 1,048
- Likes
- 3
- Commercial use allowed
- Yes
- Derivatives allowed
- Yes
- Gated
- No
- Last modified
- 2026-04-03T03:35:42+00:00
- Library name
- transformers
- Downloads all time
- 20,845
- Model type
- hybrid_qwen3
- Redistribution allowed
- Yes
- Tags
- transformers, safetensors, hybrid_qwen3, text-generation, hybrid, ssm, state-space-model, linear-attention, gated-kalmanet, priming, long-context, reasoning
- Weights available
- Yes
- Weights dtype
- BF16
Release daterelease_date1
Opennessopenness1
Licenselicense1
Architecturearchitecture1
Parametersparameter_count1
Base modelbase_model1
File sizefile_size_gb1
Hugging Face repohf_repo1
Pipeline tagpipeline_tag1
Model cardmodel_card_url1
Downloadsmetric.downloads1
Likesmetric.likes1
Gatedgated1
Last modifiedlast_modified1
Library namelibrary_name1
Downloads all timemetric.downloads_all_time1
Model typemodel_type1
Weights dtypeweights_dtype1
Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →
Provenance
Attributed facts
19
Source tiers
T219
Freshest observation
7 h ago
Conflicts
None
Source documents 3
Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.
Data quality (53/100) measures how well AI Atlas knows this entity — completeness, primary-source ratio, freshness, conflicts — never how good the model is. Methodology →