Wonder-Griffin
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TraXLMistralForCausalLM

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 ---
 library_name: transformers
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 ---
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 ---
+license: wtfpl
+datasets:
+- Biddls/Onion_News
+language:
+- en
+metrics:
+- f1
+- accuracy
+- precision
+- perplexity
+base_model:
+- Wonder-Griffin/TraXL
 library_name: transformers
 ---
+TraXLMistral
+Created by: Morgan Griffin & WongrifferousAI (Wonder-Griffin)
+#Model Description
+TraXLMistral is a custom language model based on the GPT-2 architecture with additional enhancements for various tasks including causal language modeling, sequence classification, and question answering. The model incorporates several advanced techniques such as sparse attention, memory-augmented neural networks (MANN), adaptive computation time (ACT), and latent space clustering, making it suitable for both reasoning and general-purpose text generation.
+#Key Features:
+Sparse Attention: Efficient attention mechanism inspired by Mistral, focusing computational resources on important elements in the sequence.
+Memory-Augmented Neural Networks (MANN): Enhances model capacity by adding external memory to better handle long-term dependencies and complex reasoning tasks.
+Adaptive Computation Time (ACT): Dynamically adjusts the number of computation steps based on the complexity of the input.
+Latent Space Clustering: Clusters latent representations for improved interpretability and task-specific adjustments.
+Logical Transformer Layer: Improves the model's reasoning capabilities by integrating logical transformations.
+Intended Uses & Limitations
+#Use Cases:
+Text Generation: Generating coherent and contextually relevant text in a wide range of domains, including conversational agents, story generation, and creative writing.
+Question Answering: Providing accurate and concise answers to natural language questions.
+Sequence Classification: Classification of text into predefined categories such as sentiment analysis, document categorization, or other NLP tasks.
+Conversational AI: Suitable for applications requiring interactive and context-aware conversation.
+#Limitations:
+This model may require additional fine-tuning for domain-specific tasks where the input data differs significantly from the training data.
+Due to the use of sparse attention and memory modules, the model may require more resources (GPU memory) compared to simpler architectures.
+Training Procedure
+The model was trained using the Wikitext-raw-01 dataset (details needed) and fine-tuned for various tasks such as causal language modeling, question answering, and sequence classification. #Training Hyperparameters:
+Learning Rate: 5e-05
+Train Batch Size: 8
+Eval Batch Size: 8
+Optimizer: Adam (betas = (0.9, 0.999), epsilon = 1e-08)
+LR Scheduler: Linear
+Training Steps: 100,000
+Seed: 42
+#Training Environment:
+Transformers version: 4.45.0.dev0
+PyTorch version: 2.4.0+cu124
+Datasets version: 2.20.0
+Tokenizers version: 0.19.1
+GPU: The model is trained using GPU acceleration, with checks for CUDA availability and multiple GPUs.
+Model Architecture
+##Configuration:
+Model Type: Hybrid Transformer with GPT/Mistral/TransformerXL (Causal LM)
+Vocab Size: 50256
+Hidden Size: 768
+Number of Layers: 4
+Number of Attention Heads: 4
+Feedforward Expansion Factor: 4
+RNN Units: 128
+Max Sequence Length: 256
+Dropout Rate: 0.1
+Sparse Attention: Enabled
+Memory Size: 256
+Max Computation Steps: 5
+Dynamic Routing: Enabled
+##Special Modules:
+Sparse Attention Layer: Improves efficiency by reducing unnecessary attention computation.
+Adaptive Computation Time (ACT): Adjusts computation time based on input complexity.
+Memory-Augmented Neural Networks (MANN): Provides external memory to help with long-term dependencies.
+Latent Space Clustering: Clusters latent representations for improved task-specific behavior.
+Logical Transformer Layer: Improves reasoning and logic-based tasks.
+##Supported Tasks:
+Causal Language Modeling (causal_lm): Generates text sequences based on a given prompt.
+Question Answering (qa): Extracts relevant answers from a context given a question.
+Sequence Classification: Classifies input sequences into one of the predefined labels.
+##Evaluation##
+The model was evaluated on several NLP benchmarks, but detailed results are pending. The primary metrics used for evaluation include accuracy, F1-score, and precision. Evaluation Metrics:
+Accuracy
+F1-score
+Precision
+Intended Users
+This model is designed for researchers, developers, and organizations looking to implement advanced NLP models in production. It can be used for building conversational agents, question-answering systems, text generation applications, and more. How to Use Inference Example """"
+python
+from transformers import BertTokenizerFast, TraXLMistral
+tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased') model = TraXLMistral.from_pretrained('Wonder-Griffin/TraXLMistral')
+input_text = "What is the capital of France?" inputs = tokenizer(input_text, return_tensors="pt") outputs = model.generate(**inputs) print(outputs) """" Limitations and Future Work
+Limited Training Data: Future iterations should focus on expanding the dataset and improving performance across different languages and domains.
+Memory Usage: Due to its complex architecture, this model might require optimizations for resource-constrained environments.
+Acknowledgements
+**Created by Morgan Griffin and WongrifferousAI (Wonder-Griffin)**