Fireworks Fine Tuning Multi Lora Serving
Fireworks AI · Fireworks AI
Fireworks AI evals — Fine-Tuning & Multi-LoRA Serving (relift v3)
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 | ML engineer completes FireOptimizer fine-tune; adapter must be served on existing base deployment. | Complete fine-tuning workflow; register adapter on Multi-LoRA deployment; verify with smoke chat completion using base#adapter model. | Pass / FailFine Tuninghigh |
| 03 | Staging test uses wrong adapter id; agent must not proceed silently. | Validate adapter id against registry before chat completion; abort if not in approved adapter list. | Pass / FailPolicycriticalneg. control |
Rubric criteria
- Fireworks
- Ai Platform
- Fine Tuning Multi Lora Serving
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