Text Classification
PEFT
Safetensors
English
Korean
rlcd
system-1
non-autoregressive
decision-model
reward-model
llm-judge
calibrated-decisions
kev
qwen3.5
reinforcement-learning
fast-inference
ultra-low-latency
Instructions to use gyung/Qwev-9B-RLCD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use gyung/Qwev-9B-RLCD with PEFT:
from peft import PeftModel from transformers import AutoModel base_model = AutoModel.from_pretrained("Qwen/Qwen3.5-9B-Base") model = PeftModel.from_pretrained(base_model, "gyung/Qwev-9B-RLCD") - Notebooks
- Google Colab
- Kaggle
Commit History
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Update README.md
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Update README.md
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docs: fix model card math formatting, remove tildes (avoid strikethrough), and correct CMU benchmark attribution
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Upload README.md with huggingface_hub
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Upload README.md with huggingface_hub
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Upload README.md with huggingface_hub
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docs: add vanilla Kev-9B baseline performance row in CMU comparison table
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docs: add self-contained RLCD loss code, architecture lineage, and CMU benchmarks
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docs: add detailed dataset composition and methodology
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docs: publish comprehensive bilingual model card with CMU Table 1 SOTA benchmarks
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docs: update comprehensive model card with CMU Table 1 benchmark results
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feat: upload gyung/Qwev-9B-RLCD aligned with CMU RLCD on A100 GPU
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initial commit
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