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README.md

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-license: apache-2.0
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+Model Card for CoreX v0.1
+
+This model card documents CoreX v0.1, a lightweight transformer-based language model developed by Nexizan Company. CoreX is optimized for low-memory systems while enabling offline AI assistants, coding tutors, and sandbox research.
+
+Model Details
+Model Description
+
+Developed by: Nexizan Company
+
+Funded by [optional]: Self-funded
+
+Shared by [optional]: Nexizan Company CoreX team
+
+Model type: Decoder-only Transformer (causal LM)
+
+Language(s) (NLP): English
+
+License: Apache-2.0
+
+Finetuned from model [optional]: Trained from scratch
+
+Model Sources [optional]
+
+Repository: [To be added]
+
+Paper [optional]: N/A
+
+Demo [optional]: Local chat interface (chat_interface.py)
+
+Uses
+Direct Use
+
+Conversational assistant (terminal interface)
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+Text generation and summarization
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+Code and math assistance
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+Educational / research sandbox
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+Downstream Use [optional]
+
+Fine-tuning for domain-specific tasks (education, productivity, research)
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+Integration into private offline-first AI platforms (e.g., NexIN)
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+Out-of-Scope Use
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+Medical, legal, or financial decision-making
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+Fully autonomous deployment without human oversight
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+Generating harmful or unsafe content
+
+Bias, Risks, and Limitations
+
+Trained on ~9.2M tokens → knowledge is limited compared to larger models
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+Performance weaker in non-English languages
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+May reproduce biases from the dataset
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+Can generate hallucinated or incorrect facts
+
+Recommendations
+
+Always use human oversight for critical applications
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+Apply filtering or moderation layers for safety
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+Fine-tune with curated datasets for better domain performance
+
+How to Get Started with the Model
+python chat_interface.py
+
+
+Or in Python:
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("path/to/corex_tok.model")
+model = AutoModelForCausalLM.from_pretrained("path/to/final_model.pt")
+
+inputs = tokenizer("Hello CoreX!", return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=50)
+print(tokenizer.decode(outputs[0]))
+
+Training Details
+Training Data
+
+Samples: 34,559
+
+Tokens: ~9.2M
+
+Avg length: ~266 tokens
+
+Max length: 1024 tokens
+
+Tokenizer: SentencePiece unigram, vocab size 32,000
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+Preprocessing [optional]
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+Normalization and whitespace handling
+
+Special tokens for <pad>, <unk>, <s>, </s>
+
+Training Hyperparameters
+
+Training regime: Mixed precision (CPU/GPU optimized)
+
+Hidden size: 512
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+Layers: 8
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+Attention heads: 8 (2 key-value heads)
+
+Intermediate size: 1365 (SwiGLU)
+
+Max position embeddings: 2048
+
+Learning rate: 5e-4 (cosine schedule)
+
+Optimizer: AdamW (β1=0.9, β2=0.95, wd=0.1)
+
+Batch size: 2 (accumulated to 32)
+
+Steps: 50,000
+
+Speeds, Sizes, Times [optional]
+
+Parameters: ~54.8M
+
+Checkpoint size: ~220MB
+
+Optimized for: ~7GB RAM systems
+
+Evaluation
+Testing Data, Factors & Metrics
+Testing Data
+
+Evaluation with held-out samples from the same dataset
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+Factors
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+Tested on conversational, code, and math-style prompts
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+Metrics
+
+Perplexity (PPL) and training loss
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+Results
+
+PPL: decreasing across training (exact final values TBD)
+
+Baseline evaluation shows fluent short-text generation
+
+Summary
+
+CoreX v0.1 demonstrates solid performance for a lightweight model on low-resource hardware but is not competitive with large-scale LLMs.
+
+Model Examination [optional]
+
+Architecture verified with rotary embeddings, grouped query attention, SwiGLU, and RMSNorm.
+
+Environmental Impact
+
+Hardware Type: Consumer GPU/CPU
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+Hours used: Few days of training
+
+Cloud Provider: None (local)
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+Compute Region: Local system
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+Carbon Emitted: Low (small model size)
+
+Technical Specifications [optional]
+Model Architecture and Objective
+
+Decoder-only transformer, 8 layers, SwiGLU, GQA, RoPE
+
+Compute Infrastructure
+
+Hardware: ~7GB RAM device (tested on consumer GPU/CPU)
+
+Software: PyTorch, SentencePiece
+
+Citation [optional]
+
+BibTeX:
+
+@misc{corex2025,
+  title={CoreX v0.1: Lightweight Transformer Language Model},
+  author={Nexizan Company},
+  year={2025},
+  license={Apache-2.0}
+}
+
+
+APA:
+Nexizan Company. (2025). CoreX v0.1: Lightweight Transformer Language Model.
+
+Glossary [optional]
+
+RoPE: Rotary Position Embedding
+
+SwiGLU: Swish-Gated Linear Unit
+
+RMSNorm: Root Mean Square Normalization
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+GQA: Grouped Query Attention
+
+More Information [optional]
+
+CoreX is intended as a stepping stone toward future versions with larger parameter counts and better datasets.
+
+Model Card Authors [optional]
+
+Nexizan Company CoreX Team
+
+Model Card Contact
+
+N/A