Aya Expanse 32B

Cost & access

Aya Expanse 32B weights are available under Proprietary. Direct API cost may be $0 if you self-host; budget for GPUs, storage, and engineering instead. Hosted endpoints (Together, Fireworks, Groq, etc.) charge per token—shop providers for aya-expanse-32b latency and region. With a 128K tokens context window, long PDFs or chat histories increase input tokens quickly—trim history or summarize older turns in production.

Functional understanding

• Multilingual open model from Cohere.
• Modalities: text · License: Proprietary · Released 2024-10-24.
• Best-fit workflows for this model:
• • Drafting, summarization, and structured extraction from long documents.
• • On-prem or VPC deployment when data cannot leave your network.

Technical foundation

• Cohere reports Undisclosed parameters; training data: Undisclosed.
• Context: 128K tokens. Open weights: yes.
• Aya Expanse 32B is positioned as a general-purpose model in the Cohere lineup.

First API call

Install cohere and call Aya Expanse 32B via chat or embed endpoints per Cohere docs.

import cohere

co = cohere.ClientV2("YOUR_API_KEY")
resp = co.chat(
    model="aya-expanse-32b",
    messages=[{"role": "user", "content": "Hello from Aya Expanse 32B"}],
)
print(resp.message.content[0].text)

Important technical topics

• Prompting Aya Expanse 32B: be explicit about output format. Weak: "Analyze this." Better: "Return JSON with fields id, total, date for Cohere billing data."
• Temperature: use 0–0.3 for extraction and compliance on Aya Expanse 32B; 0.7–1.0 for brainstorming.
• Tokens: Aya Expanse 32B bills by tokens (~¾ word each). Undisclosed parameters affect capability; your bill is driven by context length and call volume.
• Context window (128K tokens): everything in one request—system prompt, tools, RAG chunks, and history—must fit. Truncate or summarize when approaching the limit for Aya Expanse 32B.

Real enterprise patterns

• RAG with Aya Expanse 32B: retrieve from your vector DB, cite sources in the prompt.
• Tool calling: define JSON schemas; let Aya Expanse 32B request functions, not free-form SQL.
• Eval suite: regression prompts before each model or prompt change.
• Cost routing: default to Aya Expanse 32B for hard tasks; smaller sibling model for triage.

Production & security

• Secrets: never commit keys for Aya Expanse 32B; use vault + per-environment rotation.
• PII: mask before inference; log redacted prompts only.
• Observability: trace id per request; log model=aya-expanse-32b, tokens in/out, latency.
• GPU monitoring: VRAM, batch queue depth, and model revision hash on each deploy.
• Guardrails: schema-validate JSON; block disallowed topics; cross-check numbers against source docs.

Mini projects with this model

• Support copilot: Aya Expanse 32B drafts replies from KB snippets.
• Contract clause extractor with human approval.
• Weekly metrics narrative from SQL + CSV exports.
• Agent that files expenses from receipt photos (if multimodal).

Suggested stack

• Language: Python 3.11+
• Model: Aya Expanse 32B via Ollama, vLLM, or Hugging Face
• Hardware: NVIDIA GPU with enough VRAM for quantization level
• API wrapper: FastAPI or LiteLLM proxy
• UI: Streamlit or Next.js for internal tools
• APIs: FastAPI
• Vector DB (RAG): Pinecone / Chroma / pgvector

Learning path

• Python basics
• HTTP/REST and environment variables
• Cohere authentication and Aya Expanse 32B model id (aya-expanse-32b)
• First successful call to Aya Expanse 32B
• Prompt design and JSON / structured outputs
• RAG
• Tool use / function calling
• Evals and regression sets
• Production deploy + monitoring