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ZAYA1-8B
ZAYA1-8B is a small Zyphra mixture-of-experts reasoning model with public weights, 760M active parameters, 8.4B total parameters, deployment notes, and project-reported math and coding evaluations.
The official Hugging Face model card presents ZAYA1-8B as the post-trained reasoning version of Zyphra's ZAYA1 model family, with safetensors files, benchmark tables, quickstart notes, vLLM and Transformers branch requirements, a vLLM serving example, and links to Zyphra's technical report and release blog post. Use this as a first read, not a recommendation. Open the original project before trusting details like terms, limits, privacy, cost, setup, or safety.
What it is
A compact MoE reasoning model
ZAYA1-8B is framed around reasoning efficiency: a model with under one billion active parameters per token while retaining a larger total-parameter MoE structure for math, coding, and long-form reasoning tasks.
Why it stands out
Small-model reasoning focus
The official materials emphasize architecture and post-training work, project-reported evaluation results, on-device or local-application potential, and serving through Zyphra-specific branches of common inference libraries.
Availability
Model card, files, report, and deployment notes
Readers can inspect the Hugging Face model card, download model files, review the benchmark tables, read Zyphra's release materials, and study the vLLM or Transformers setup notes before trying it.
Why it matters
Why readers may notice it
Efficient reasoning models are becoming a practical comparison point for builders who care about capability, serving cost, latency, and local deployment. It gives readers another way to compare whether smaller active-parameter models can handle harder math and coding work without jumping straight to much larger systems.
What readers may want to know
Where it fits
Open it as part of the model layer. It is most relevant for readers comparing small MoE models, reasoning-oriented releases, coding and math benchmarks, local LLM applications, test-time compute approaches, and serving tradeoffs for compact models.
Reporting note
What appears notable
Source materials point to the 760M-active and 8.4B-total parameter framing, post-trained reasoning release, project-reported benchmark tables, technical report, Zyphra blog post, on-device/local application note, and deployment guidance that currently depends on Zyphra branches of vLLM or Transformers.
Before using
What readers may want to review
The quickstart requirements, including Python environment expectations and the Zyphra branches of vLLM or Transformers mentioned by the model card.
The project-reported evaluation tables and comparison setup before treating benchmark numbers as complete deployment guidance.
Hardware, memory, serving, local-deployment, and on-device assumptions before using it in a real application or agent workflow.
Reader fit
Who may find it relevant
Readers comparing efficient reasoning models for math, coding, and longer-form problem solving.
Builders exploring compact MoE serving, local LLM applications, vLLM deployment, or test-time compute workflows.
Less relevant for readers looking for a browser agent, RAG platform, speech model, or no-setup consumer chatbot.
Editorial note
Why it is included here
This entry is here because ZAYA1-8B gives readers a current small-MoE reasoning model to compare against larger reasoning releases, especially around math, coding, serving efficiency, local use, and project-reported evaluation claims.
Source links
Original materials
Reader note
Before relying on this entry
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