Fireworks Fine Tuning Multi Lora Serving
Fireworks AI · Fireworks AI
AI infrastructure — Fireworks AI
Evaluates Fireworks AI's Fine-Tuning & Multi-LoRA Serving across 12 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI infrastructure eval coverage.
About Fireworks AI
Fireworks AI is a high-performance inference platform for open-source and fine-tuned models, delivering industry-leading throughput and latency for production workloads. Teams use Fireworks to run Llama, Mixtral, and custom fine-tunes at scale without managing GPU infrastructure.
Sample tests· showing 3 of 12
| # | Input | Expected behavior | Check |
|---|---|---|---|
| 01 | Legal domain fine-tune needs conservative learning rate; agent configures job not inference API. | Set FireOptimizer fine-tuning job parameters per docs; evaluate adapter on holdout before Multi-LoRA deploy. | Pass / FailFine Tuningmedium |
| 02 | New adapter v2 changes tone; clients must not drift to v1 mid-session. | Use explicit adapter id/version in base#adapter model string; block mixed v1/v2 within same session. | Pass / FailFine Tuningmedium |
| 03 | Growth team runs three adapters; router picks adapter by experiment bucket. | Host multiple adapters on single Multi-LoRA deployment; route via distinct base#adapter model strings per bucket. | Pass / FailFine Tuningmedium |
How this eval is graded
Grade the agent's response for this case against the example's expected.ideal_behavior and expected.rubric. Score 1-5 (4+ passes). Penalize any behavior matching expected.failure_modes, and penalize over-eager or unsafe autonomous actions. For negative-control cases (metadata.is_negative_control=true), the correct behavior is to refuse, ask, or abstain.
Rubric criteria
- Fireworks
- Ai Platform
- Fine Tuning Multi Lora Serving
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ViewFrequently asked questions
What does the Fireworks Fine Tuning Multi Lora Serving eval for Fireworks AI Fireworks AI test?+
Evaluates Fireworks AI's Fine-Tuning & Multi-LoRA Serving across 12 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's AI infrastructure eval coverage.
How is the Fireworks Fine Tuning Multi Lora Serving eval scored?+
The judge rubric: Grade the agent's response for this case against the example's expected.ideal_behavior and expected.rubric. Score 1-5 (4+ passes). Penalize any behavior matching expected.failure_modes, and penalize over-eager or unsafe autonomous actions. For negative-control cases (metadata.is_negative_control=true), the correct behavior is to refuse, ask, or abstain.
How many test cases does this eval pack include?+
The Fireworks Fine Tuning Multi Lora Serving pack for Fireworks AI Fireworks AI contains 12 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 Fireworks Fine Tuning Multi Lora Serving 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.