Cerebras Inference

Skills

• cerebras-metrics
  Set up Prometheus scraping and Grafana dashboards for Cerebras dedicated inference endpoints. Use when configuring observability for dedicated endpoints, building monitoring dashboards, or integrating with Prometheus/Grafana Cloud/Datadog.
• cerebras-models
  Discover models available on Cerebras Inference and migrate workloads between them. Use when listing available models, checking rate limits per tier, or converting an existing workload to a different model.
• cerebras-openai-compatibility
  Use the OpenAI SDK or OpenAI-compatible clients with Cerebras by swapping the base URL. Use when migrating from OpenAI, or using Cerebras with OpenAI-compatible tooling.
• cerebras-output-control
  Control Cerebras Chat Completions output using stop sequences, frequency/presence penalties, temperature, and sampling parameters. Use when filtering phrases, tuning determinism, or adjusting response creativity.
• cerebras-payload-optimization
  Reduce TTFT on the Cerebras API by compressing request payloads with gzip or msgpack. Use when benchmarking compression strategies, measuring prompt size in tokens, or optimizing large chat payloads.
• cerebras-prompt-caching
  Measure and optimize Cerebras automatic prompt caching. Use when benchmarking cache hit rate, understanding prompt_cache_key scoping, or analyzing TTFT reduction from warm caches.
• cerebras-rate-limits
  Use Cerebras rate limit response headers to maximize throughput and avoid 429 errors. Use when building clients that resume as soon as limits reset, or debugging unexpected rate limiting behavior.
• cerebras-reasoning
  Configure and benchmark reasoning on Cerebras models (gpt-oss-120b, zai-glm-4.7). Use when testing reasoning formats, measuring performance across effort levels, or debugging multi-turn reasoning retention.
• cerebras-structured-outputs
  Enforce JSON schema compliance on Cerebras model responses using strict mode. Use when debugging schema validation errors, checking strict=true compatibility, or migrating from JSON mode to structured outputs.
• cerebras-tool-use
  Implement tool calling with Cerebras models, including parallel tool calls and end-to-end latency measurement. Use when building agentic workflows, benchmarking parallel vs. sequential tool calls, or debugging tool call schemas.

How to call

https://inference-docs.cerebras.ai

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