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--- |
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language: |
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- en |
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tags: |
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- deval |
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- evaluation |
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- llama |
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library_name: transformers |
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model-index: |
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- name: roadz/dv-finetuned-211124 |
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results: [] |
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pipeline_tag: text-generation |
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--- |
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# Model Card for roadz/dv-finetuned-211124 |
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This model is fine-tuned for evaluating LLM outputs in RAG scenarios, focusing on: |
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- Hallucination detection |
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- Attribution accuracy |
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- Summary completeness |
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- Response relevancy |
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## Model Details |
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### Model Architecture |
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- Base Model: LLaMA-3.1-8B |
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- Architecture Type: llama |
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- Parameters: Not specified |
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- Training Type: Fine-tuned for evaluation |
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### Hardware Requirements |
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- Minimum GPU Memory: 16GB |
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- Recommended GPU Memory: 24GB |
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- Format: SafeTensors |
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## Usage |
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This model is designed for the De-Val subnet and requires specific pipeline code for evaluation tasks. |
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### Generation Configuration |
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- Max Length: Not specified |
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- Temperature: 0.6 |
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- Top-p: 0.9 |
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- Top-k: 50 |
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## Training |
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The model was fine-tuned on evaluation tasks including: |
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- Hallucination detection scenarios |
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- Attribution verification tasks |
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- Summary completeness assessment |
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- Response relevancy evaluation |
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## Limitations |
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- Designed specifically for evaluation tasks |
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- Requires De-Val pipeline code |
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- Not intended for general text generation |
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## Last Updated |
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2024-11-21 |
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