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Qwen3-8B-BeyondReality

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lolzinventor commited on Sep 23, 2025

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-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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 ## Model Details
-### Model Description
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-## Uses
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 ---
+license: apache-2.0
+base_model:
+- Qwen/Qwen3-8B
 ---
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/67a020f79102e9be6460b24b/abGtYDgdc3Nam34mjfHsQ.png)
+# Model Card for Beyond Reality
 ## Model Details
+### Basic Information
+- **Model Type:** Language Model
+- **Base Model:** Qwen/Qwen3-8B
+- **Training Type:** Fine-tuned
+- **Version:** 1.0
+- **Language(s):** English
+### Model Architecture
+- Architecture: Qwen/Qwen3-8B
+- Parameters: 8 billion
+- Training Procedure: Fine-tuned on a custom dataset of interactive fiction scenarios
+## Intended Use
+- **Primary intended uses:** Interactive storytelling, text-based adventure games, narrative exploration
+- **Primary intended users:** Game developers, writers, AI researchers, interactive fiction enthusiasts
+## Limitations and Bias
+- Limited to 5-6 coherent actions in sequence before potential degradation
+- May exhibit biases present in the original LLaMA model and the fine-tuning dataset
+- Not suitable for factual information retrieval or real-world decision making
+## Training Data
+Fine-tuned on a proprietary dataset of interactive fiction scenarios, featuring:
+- Multi-choice action systems (options A-D)
+- Custom user-defined actions (E+)
+- Various narrative genres and settings
+## Performance and Evaluation
+- Maintains coherence for 5-6 sequential actions on average
+- Evaluated primarily through user testing and narrative consistency
+- Already starting as a small model, quantization noticeably reduces the ability of the model to follow the multiple-choice narrative
+## Ethical Considerations
+- Model outputs are fictional and should not be used as a source of factual information
+- Users should be aware of potential biases in generated content