Search ideas for Qwen/Qwen2.5-Coder-1.5B-Instruct (Hugging Face)

Qwen/Qwen2.5-Coder-1.5B-Instruct on Hugging Face is a practical option for Search workflows in Osirus AI.

Open-model families provide flexible options for cost, speed, and specialization tradeoffs.

What you can build with this model

• Answer synthesis flows that produce short executive summaries plus detail blocks.
• Competitive monitoring briefs generated from recurring search snapshots.
• Grounded research workflows that blend internal docs with web context.

Why this model is a good fit

• Strong choice for experimentation across multiple model variants.
• Useful for balancing throughput and output quality across workloads.
• Good for tailored workflows where teams tune prompts per domain.
• Useful for query expansion and evidence summarization pipelines.
• Model outputs include: Text.

Build flow in Osirus UI

1. Open /search in Osirus and select Qwen/Qwen2.5-Coder-1.5B-Instruct from Hugging Face.
2. Define retrieval scope (docs, web, or blended) before prompt tuning.
3. Add a query-rewrite stage for better recall and cleaner evidence.
4. Force answer sections for findings, evidence, and unresolved questions.
5. Save the final workflow as a repeatable pattern for your team.

Starter prompts

• Rewrite this user query into five focused search queries for deeper retrieval coverage.
• Given these retrieved passages, produce an answer with assumptions, evidence, and confidence level.
• Create an executive summary from search results with one-paragraph recommendations.

Production checklist

• Evaluate outputs against completeness and contradiction metrics.
• Add source freshness expectations for time-sensitive topics.
• Enforce citation rules for high-stakes summaries.
• Separate retrieval quality checks from answer quality checks.
• Benchmark several variants for your exact task before locking defaults.

Open this model in Osirus and turn one of these ideas into a reusable team workflow.