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README.md
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- trl
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- llama3.2
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- Reinforcement learning
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# **Bellatrix-Tiny-3B-R1**
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2. **Dependence on Training Data**: It is only as good as the quality and diversity of its training data, which may lead to biases or inaccuracies.
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3. **Computational Resources**: The model’s optimized transformer architecture can be resource-intensive, requiring significant computational power for fine-tuning and inference.
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4. **Language Coverage**: While multilingual, some languages or dialects may have limited support or lower performance compared to widely used ones.
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5. **Real-World Contexts**: It may struggle with understanding nuanced or ambiguous real-world scenarios not covered during training.
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- trl
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- llama3.2
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- Reinforcement learning
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- SFT
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# **Bellatrix-Tiny-3B-R1**
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2. **Dependence on Training Data**: It is only as good as the quality and diversity of its training data, which may lead to biases or inaccuracies.
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| 67 |
3. **Computational Resources**: The model’s optimized transformer architecture can be resource-intensive, requiring significant computational power for fine-tuning and inference.
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| 68 |
4. **Language Coverage**: While multilingual, some languages or dialects may have limited support or lower performance compared to widely used ones.
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| 69 |
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5. **Real-World Contexts**: It may struggle with understanding nuanced or ambiguous real-world scenarios not covered during training.
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