pszemraj / rnj-1.5-instruct

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Total runs: 28
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7-day runs: 0
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Model's Last Updated: June 14 2026
text-generation

Introduction of rnj-1.5-instruct

Model Details of rnj-1.5-instruct

rnj-1.5-instruct (sliding-window patched)

Drop-in transformers repack of EssentialAI/rnj-1.5-instruct intended for quick inference tests, downstream software compatiblity ( transformers/torch itself, llama.cpp, etc ):

  • Upstream declares layer_types: chunked_attention , which modeling_gemma3.py doesn't implement - inference crashes with KeyError: 'chunked_attention' . This repo swaps those entries to sliding_attention so the model loads under stock transformers . Weights unchanged, resaved in bf16.

Sliding window (8192) is not identical to the original block-local attention - equivalent for prompts ~< 8192 tokens, divergent beyond that. For faithful long-context inference, use vLLM 0.20.0 against the upstream repo.

Changes 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
import torch
from transformers import pipeline

pipe = 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.

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28
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