Text Classification
Transformers
Safetensors
English
qwen2
reward-model
code-generation
rlhf
text-embeddings-inference
Instructions to use Rishubi/CodeRM-NT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rishubi/CodeRM-NT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Rishubi/CodeRM-NT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Rishubi/CodeRM-NT") model = AutoModelForSequenceClassification.from_pretrained("Rishubi/CodeRM-NT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# CodeRM-NT
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[Paper](
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[Github](https://github.com/THUDM/CodeRM-NT)
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Providing accurate reward signals for code generated by LLMs is a significant challenge in applying reinforcement learning (RL) to code generation. Existing methods rely on unit tests, which are expensive to curate and unreliable when automatically synthesized.
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# CodeRM-NT
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[Paper](https://github.com/THUDM/CodeRM-NT/blob/main/assets/CodeRM-NT.pdf) |
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[Github](https://github.com/THUDM/CodeRM-NT)
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Providing accurate reward signals for code generated by LLMs is a significant challenge in applying reinforcement learning (RL) to code generation. Existing methods rely on unit tests, which are expensive to curate and unreliable when automatically synthesized.
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