Create README.md

README.md

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+---
+license: mit
+datasets:
+- carlosejimenez/wikitext__wikitext-2-raw-v1
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- torch
+- Tkinking
+---
+
+# Latent Recurrent Depth Language Model
+
+## Model Description
+
+This model is a Latent Recurrent Depth Language Model (LRD-LM), an experimental architecture designed for text generation.  It combines a "prelude" block for initial processing, a recurrent block with a latent state, and a "coda" block for final output.  The recurrent block allows for multiple iterations over the input sequence, potentially capturing deeper contextual information.
+
+
+## Intended Uses & Limitations
+
+**Intended Uses:**
+
+* Text generation:  The primary purpose of this model is to generate text given a prompt.  It can potentially be fine-tuned for specific tasks like creative writing, code generation, or dialogue generation.
+* Research: This model serves as an exploration of novel architectures for language modeling, potentially leading to more effective methods for capturing long-range dependencies.
+
+**Limitations:**
+
+* Data limitations: The model has been trained on a small subset of the Wikitext-2-raw dataset. Performance may be limited compared to models trained on larger, more diverse corpora.
+* Performance:  While the model demonstrates basic text generation capabilities, its overall performance is likely inferior to established state-of-the-art language models.  The provided training loop and hyperparameters are a starting point and may require significant adjustments for optimal results.
+* Computational cost: The iterative nature of the recurrent block can introduce computational overhead.
+* Bias: Like all language models, this model may exhibit biases present in its training data.
+
+
+## Training Data
+
+The model was trained on a subset (first 1000 samples) of the Wikitext-2-raw-v1 dataset.  Further details regarding pre-processing and data cleaning can be found in the source code.  This limitation may reflect biases or inaccuracies in the generated output.
+
+
+## Evaluation Results
+
+No formal evaluation metrics are provided at this time.  The model's performance is primarily demonstrated through qualitative assessment of generated samples during and after training.  Further evaluation using established metrics is recommended.
+
+
+## Ethical Considerations
+
+This model is provided for research and experimental purposes.  The user is responsible for ensuring ethical usage and mitigating potential risks associated with the generated output.
+
+
+## Model Usage Instructions
+
+The model can be used for text generation via the `generate()` method. The usage of this function is demonstrated in the example script.
+
+
+## Training
+
+The model has been trained using the AdamW optimizer and a cosine annealing learning rate scheduler for a number of epochs.  The training parameters can be configured in the provided script.
+
+
+## Usage Example (Python)