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AMAImedia
/
CodeRM-GRPO-Selection-8B-NOESIS-AWQ-INT4

Text Classification
Transformers
Safetensors
qwen3
text-generation
code-reward-model
reward-model
grpo
selection
best-of-n
rlhf
awq
awq-int4
quantized
4bit
noesis
dhcf-fno
apache-2.0
text-embeddings-inference
4-bit precision
Model card Files Files and versions
xet
Community

Instructions to use AMAImedia/CodeRM-GRPO-Selection-8B-NOESIS-AWQ-INT4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use AMAImedia/CodeRM-GRPO-Selection-8B-NOESIS-AWQ-INT4 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="AMAImedia/CodeRM-GRPO-Selection-8B-NOESIS-AWQ-INT4")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("AMAImedia/CodeRM-GRPO-Selection-8B-NOESIS-AWQ-INT4")
    model = AutoModelForCausalLM.from_pretrained("AMAImedia/CodeRM-GRPO-Selection-8B-NOESIS-AWQ-INT4")
  • Notebooks
  • Google Colab
  • Kaggle
CodeRM-GRPO-Selection-8B-NOESIS-AWQ-INT4
6.11 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
AMAImedia's picture
AMAImedia
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  • .gitattributes
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  • LICENSE
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  • README.md
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  • chat_template.jinja
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  • config.json
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  • generation_config.json
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  • model-00001-of-00002.safetensors
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  • model-00002-of-00002.safetensors
    2.1 GB
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  • model.safetensors.index.json
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  • noesis_provenance.json
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  • tokenizer.json
    11.4 MB
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  • tokenizer_config.json
    419 Bytes
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