Yi-Large

Cost & access

Check 01.AI's license (Proprietary / Yi License (open tiers)) and pricing for Yi-Large. With a 32K tokens context window, long PDFs or chat histories increase input tokens quickly—trim history or summarize older turns in production.

Functional understanding

• Bilingual Chinese-English frontier model from 01.AI with API and open smaller variants.
• Modalities: text · License: Proprietary / Yi License (open tiers) · Released 2024-05-13.
• Best-fit workflows for this model:
• • Drafting, summarization, and structured extraction from long documents.

Technical foundation

• 01.AI reports Undisclosed parameters; training data: Undisclosed.
• Context: 32K tokens. Open weights: no.
• Yi-Large is positioned as a general-purpose model in the 01.AI lineup.

First API call

Follow 01.AI's official SDK for Yi-Large; use model id "yi-large" from their docs.

# See https://01.ai/
# Model id: yi-large

Important technical topics

• Prompting Yi-Large: be explicit about output format. Weak: "Analyze this." Better: "Return JSON with fields id, total, date for 01.AI billing data."
• Temperature: use 0–0.3 for extraction and compliance on Yi-Large; 0.7–1.0 for brainstorming.
• Tokens: Yi-Large bills by tokens (~¾ word each). Undisclosed parameters affect capability; your bill is driven by context length and call volume.
• Context window (32K tokens): everything in one request—system prompt, tools, RAG chunks, and history—must fit. Truncate or summarize when approaching the limit for Yi-Large.

Real enterprise patterns

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

Production & security

• Secrets: never commit keys for Yi-Large; use vault + per-environment rotation.
• PII: mask before inference; log redacted prompts only.
• Observability: trace id per request; log model=yi-large, tokens in/out, latency.
• Rate limits: handle 01.AI 429/5xx with exponential backoff and circuit breakers.
• Guardrails: schema-validate JSON; block disallowed topics; cross-check numbers against source docs.

Mini projects with this model

• Support copilot: Yi-Large 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+
• LLM: Yi-Large (01.AI official SDK)
• 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
• 01.AI authentication and Yi-Large model id (yi-large)
• First successful call to Yi-Large
• Prompt design and JSON / structured outputs
• RAG
• Tool use / function calling
• Evals and regression sets
• Production deploy + monitoring