together-batch-inference
High-volume, asynchronous offline inference at up to 50% lower cost via Together AI's Batch API. Prepare JSONL inputs, upload files, create jobs, poll status, and download outputs. Reach for it whenever the user needs non-interactive bulk inference rather than real-time chat or evaluation jobs.
What this skill does
# Together Batch Inference ## Overview Use Together AI's Batch API for large offline workloads where latency is not the primary concern. Typical fits: - bulk classification - synthetic data generation - dataset transformations - large summarization or enrichment jobs - low-cost asynchronous inference ## When This Skill Wins - The user has many independent requests to run - A JSONL request file is acceptable - Turnaround time can be minutes or hours instead of seconds - Lower cost matters more than immediate interactivity ## Hand Off To Another Skill - Use `together-chat-completions` for real-time requests or tool-calling apps - Use `together-evaluations` for managed LLM-as-a-judge workflows - Use `together-embeddings` for retrieval-specific vector generation ## Quick Routing - **End-to-end batch workflow** - Start with [scripts/batch_workflow.py](scripts/batch_workflow.py) or [scripts/batch_workflow.ts](scripts/batch_workflow.ts) - **Request format, status model, and result downloads** - Read [references/api-reference.md](references/api-reference.md) - **Operational guidance and batch sizing** - Read [references/api-reference.md](references/api-reference.md) ## Workflow 1. Build a JSONL file where each line contains `custom_id` and `body`. 2. Upload the file with `purpose="batch-api"`. 3. Create the batch with `input_file_id=...` and the target endpoint. 4. Poll until the job is terminal. 5. Download output and error files, then reconcile by `custom_id`. ## High-Signal Rules - Python scripts require the Together v2 SDK (`together>=2.0.0`). If the user is on an older version, they must upgrade first: `uv pip install --upgrade "together>=2.0.0"`. - Use `input_file_id`, not legacy file parameters. - Keep `custom_id` stable and meaningful so result reconciliation is easy. - Batch is for independent requests. If the workload depends on shared conversation state, it is probably the wrong tool. - Always inspect the error file in addition to the success output. - `client.batches.create()` returns a wrapper; access the batch object via `response.job` (e.g., `response.job.id`). `client.batches.retrieve()` returns the batch object directly. - For classification or labeling workloads, set `max_tokens` low (e.g., 4), use `temperature: 0`, and constrain the system prompt to return only the label. This minimizes output tokens and cost. - Small batches (under 1K requests) typically complete in minutes. The 24-hour completion window is a maximum, not typical. ## Resource Map - **API reference and operational guidance**: [references/api-reference.md](references/api-reference.md) - **Python workflow**: [scripts/batch_workflow.py](scripts/batch_workflow.py) - **TypeScript workflow**: [scripts/batch_workflow.ts](scripts/batch_workflow.ts) ## Official Docs - [Batch Inference](https://docs.together.ai/docs/batch-inference) - [Batch API](https://docs.together.ai/reference/batch-create)
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