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ssurface
/
cot-dialect-olmo3-7b-think-grpo-reward-diff-l5

Text Generation
PEFT
Safetensors
English
lora
grpo
chain-of-thought
reasoning
compression
gsm8k
conversational
Eval Results (legacy)
Model card Files Files and versions
xet
Community

Instructions to use ssurface/cot-dialect-olmo3-7b-think-grpo-reward-diff-l5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • PEFT

    How to use ssurface/cot-dialect-olmo3-7b-think-grpo-reward-diff-l5 with PEFT:

    from peft import PeftModel
    from transformers import AutoModelForCausalLM
    
    base_model = AutoModelForCausalLM.from_pretrained("merged_olmo/l5")
    model = PeftModel.from_pretrained(base_model, "ssurface/cot-dialect-olmo3-7b-think-grpo-reward-diff-l5")
  • Notebooks
  • Google Colab
  • Kaggle
cot-dialect-olmo3-7b-think-grpo-reward-diff-l5
167 MB
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  • 1 contributor
History: 9 commits
ssurface's picture
ssurface
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  • .gitattributes
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    initial commit about 1 month ago
  • README.md
    3.94 kB
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  • adapter_config.json
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  • adapter_model.safetensors
    160 MB
    xet
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  • chat_template.jinja
    1.65 kB
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  • tokenizer.json
    7.14 MB
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  • tokenizer_config.json
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  • training_config.json
    315 Bytes
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  • training_log.jsonl
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