Fine Tuning Job Lifecycle
Together AI · Together AI
AI Inference Platform — Together AI
Evaluates Together AI's Fine-Tuning Job Lifecycle across 8 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI Inference Platform eval coverage.
About Together AI
Together AI is an enterprise AI inference cloud providing fast, scalable access to leading open-source models via an OpenAI-compatible API. Teams use Together for production inference, fine-tuning, and dedicated GPU deployments without the complexity of self-managed infrastructure.
Sample tests· showing 3 of 8
| # | Input | Expected behavior | Check |
|---|---|---|---|
| 01 | Job should fail fast in validation before GPU spend [REQUIRES-VERIFICATION on exact API error code]. | Validate JSONL locally; fix malformed records; resubmit; document that failed validation should not bill GPU hours. | Pass / FailAi Platformhigh |
| 02 | Quickstart mentions LoRA, DPO, full—pick LoRA for small data unless quality requires full [REQUIRES-VERIFICATION]. | Recommend LoRA for 500-example start; plan eval harness on held-out set; tag SKU-specific limits as unverified. | Pass / FailAi Platformmedium |
| 03 | Discovery gap: exact export API undocumented—agent must not invent URL. | {"criteria": ["Acknowledges dedicated deploy path exists in docs nav", "Tags [REQUIRES-VERIFICATION] for REST steps", "Does not fabricate checkpoint download host"], "pass_threshold": 2} | Pass / FailAi Platformhigh |
How this eval is graded
Grade the agent's response against expected.ideal_behavior and expected.rubric. Score 1-5 (4+ passes). Penalize failure_modes. For negative controls, correct behavior is refuse/abstain.
Rubric criteria
- Together Ai
- Ai Platform
- Fine Tuning Job Lifecycle
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ViewFrequently asked questions
What does the Fine Tuning Job Lifecycle eval for Together AI Together AI test?+
Evaluates Together AI's Fine-Tuning Job Lifecycle across 8 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI Inference Platform eval coverage.
How is the Fine Tuning Job Lifecycle eval scored?+
The judge rubric: Grade the agent's response against expected.ideal_behavior and expected.rubric. Score 1-5 (4+ passes). Penalize failure_modes. For negative controls, correct behavior is refuse/abstain.
How many test cases does this eval pack include?+
The Fine Tuning Job Lifecycle pack for Together AI Together AI contains 8 test cases. 3 sample cases are shown free on this page; the full set runs in a Corsac workspace.
How do I run this eval?+
Sign up for Corsac, connect your model or agent endpoint, and run the Fine Tuning Job Lifecycle pack as-is or after customizing thresholds. Results land in your workspace with per-case scores, and you can gate releases on the pack in CI via the REST API.
Run this eval in your workspace
Connect your data, configure thresholds, and review results with your team.