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-2.0Changes vs upstream
| Field | Upstream | Here |
|---|---|---|
| Local layer type | chunked_attention | sliding_attention |
| RoPE params for locals | under chunked_attention key | moved to sliding_attention key |
| Dtype | float32 | bfloat16 |
| Architecture string | Rnj1ForCausalLM | Gemma3ForCausalLM |
Local/global layer pattern (LLLGLLLGLLLGLGGGGGLGLLLGLLLGLLLL) preserved.
Usage
python
import torchfrom transformers import pipelinepipe = pipeline("text-generation",model="pszemraj/rnj-1.5-instruct",dtype=torch.bfloat16,device_map="auto",)res = pipe([{"role": "user", "content": "Who are you?"}])print(res)
License
Apache 2.0, inherited from upstream. See the original model card for architecture, benchmarks, and citation.
Model provider
pszemraj
Model tree
Base
EssentialAI/rnj-1
Fine-tuned
this model
Modalities
Input
Text
Output
Text
Pricing
Dedicated Endpoints
View detailsSupported Functionality
Model APIs
Dedicated Endpoints
Container
More information