Dedicated Endpoints
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Container
Run this model inference with full control and performance in your environment.
Get help setting up a custom Dedicated Endpoints.
Talk with our engineer to get a quote for reserved GPU instances with discounts.
README
License: apache-2.0Evaluation
Held-out sample seed 9012, k=1 smoke:
| adapter | context | seed | greedy | selected@1 | any-of-1 | single any | multi any |
|---|---|---|---|---|---|---|---|
| hard-multi/teacher-gap interp alpha 0.25 | 20k | 9012 | 8/35 | 10/35 | 11/35 | 9/18 | 2/17 |
This was a negative/non-frontier checkpoint. It loaded and evaluated cleanly, but it did not recover the teacher-gap-v1 multi-file tail hit and it underperformed the hard-multi 20k frontier.
Contents
adapter_model.safetensors: PEFT LoRA adapter weightsadapter_config.json: PEFT adapter configurationtokenizer_config.json: tokenizer configurationinterp_meta.json: local interpolation metadata
The base model weights are not included.
Model provider
imdatta0
Model tree
Base
unsloth/Qwen3-4B-Instruct-2507
Adapter
this model
Modalities
Input
Text
Output
Text
Pricing
Dedicated Endpoints
View detailsSupported Functionality
Model APIs
Dedicated Endpoints
Container
More information