Update README.md

README.md

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+license: apache-2.0
+language:
+
+en
+
 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.
+CoreX v0.1 is a lightweight, decoder-only transformer built by Nexizan Company. It is designed to run efficiently on low-resource systems (~7 GB RAM) while supporting offline AI assistants, coding tutors, and sandbox experiments.
 
 Model Details
 Model Description
 
 Developed by: Nexizan Company
 
-Funded by [optional]: Self-funded
+Funded by : Self-funded
 
-Shared by [optional]: Nexizan Company CoreX team
+Shared by : Nexizan inc *CoreX team* ( Faisal - *LitRush* )
 
-Model type: Decoder-only Transformer (causal LM)
+Model type: Causal LM (transformer, decoder-only)
 
-Language(s) (NLP): English
+Language(s): English
 
 License: Apache-2.0
 
-Finetuned from model [optional]: Trained from scratch
+Finetuned from model : None (trained from scratch)
 
-Model Sources [optional]
+Model Sources 
 
-Repository: [To be added]
+Repository: to be added
 
-Paper [optional]: N/A
+Paper: N/A
 
-Demo [optional]: Local chat interface (chat_interface.py)
+Demo: Local CLI via chat_interface.py
 
 Uses
 Direct Use
 
-Conversational assistant (terminal interface)
+Chat-based assistant (offline/terminal)
 
 Text generation and summarization
 
-Code and math assistance
+Code and math Q&A
 
-Educational / research sandbox
+Educational or personal projects
 
-Downstream Use [optional]
+Downstream Use
 
-Fine-tuning for domain-specific tasks (education, productivity, research)
+Domain-specific fine-tuning (education, productivity, private tools)
 
-Integration into private offline-first AI platforms (e.g., NexIN)
+Integration into offline AI platforms (e.g., NexIN prototype)
 
 Out-of-Scope Use
 
-Medical, legal, or financial decision-making
+Medical, financial, or legal advice
 
-Fully autonomous deployment without human oversight
+Safety-critical or autonomous systems
 
-Generating harmful or unsafe content
+Content generation without moderation
 
 Bias, Risks, and Limitations
 
-Trained on ~9.2M tokens → knowledge is limited compared to larger models
+Limited training size (~9.2M tokens) → restricted knowledge
 
-Performance weaker in non-English languages
+Biases from dataset may appear in responses
 
-May reproduce biases from the dataset
+Non-English performance is weak
 
-Can generate hallucinated or incorrect facts
+Risk of hallucinations or unsafe generations
 
 Recommendations
 
-Always use human oversight for critical applications
+Use a moderation/filtering layer in deployment
+
+Fine-tune with curated, domain-specific datasets
+
+Always keep a human-in-the-loop for sensitive applications
 
-Apply filtering or moderation layers for safety
+How to Get Started
 
-Fine-tune with curated datasets for better domain performance
+Run the interactive chat interface:
 
-How to Get Started with the Model
 python chat_interface.py
 
 
-Or in Python:
+Or load directly in Python:
 
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
@@ -94,98 +103,103 @@ Tokens: ~9.2M
 
 Avg length: ~266 tokens
 
-Max length: 1024 tokens
+Max length: 1024
 
-Tokenizer: SentencePiece unigram, vocab size 32,000
+Tokenizer: SentencePiece unigram, vocab=32,000
 
-Preprocessing [optional]
+Preprocessing
 
-Normalization and whitespace handling
+Unicode normalization
 
-Special tokens for <pad>, <unk>, <s>, </s>
+Special tokens (<pad>, <unk>, <s>, </s>)
+
+Deduplication and filtering
 
 Training Hyperparameters
 
-Training regime: Mixed precision (CPU/GPU optimized)
+Regime: Mixed precision (CPU/GPU optimized)
 
 Hidden size: 512
 
 Layers: 8
 
-Attention heads: 8 (2 key-value heads)
+Attention heads: 8 (2 KV heads)
 
 Intermediate size: 1365 (SwiGLU)
 
-Max position embeddings: 2048
+Max positions: 2048
 
-Learning rate: 5e-4 (cosine schedule)
+Learning rate: 5e-4 (cosine decay, warmup 1k steps)
 
 Optimizer: AdamW (β1=0.9, β2=0.95, wd=0.1)
 
-Batch size: 2 (accumulated to 32)
+Batch size: 2 (effective 32 with accumulation)
 
 Steps: 50,000
 
-Speeds, Sizes, Times [optional]
+Speeds, Sizes, Times
 
 Parameters: ~54.8M
 
 Checkpoint size: ~220MB
 
-Optimized for: ~7GB RAM systems
+Hardware target: 7 GB RAM systems
 
 Evaluation
-Testing Data, Factors & Metrics
 Testing Data
 
-Evaluation with held-out samples from the same dataset
+Held-out samples from training corpus
 
 Factors
 
-Tested on conversational, code, and math-style prompts
+Conversational text, code snippets, math expressions
 
 Metrics
 
-Perplexity (PPL) and training loss
+Perplexity (PPL), loss
 
 Results
 
-PPL: decreasing across training (exact final values TBD)
+Training loss decreased steadily
 
-Baseline evaluation shows fluent short-text generation
+Early tests show coherent text and code generation
 
 Summary
 
-CoreX v0.1 demonstrates solid performance for a lightweight model on low-resource hardware but is not competitive with large-scale LLMs.
+CoreX v0.1 achieves usable fluency for small-scale tasks. It is not comparable to large LLMs, but excels at lightweight, private, offline usage.
+
+Model Examination 
 
-Model Examination [optional]
+Architecture: 8-layer decoder, RoPE, SwiGLU, RMSNorm, GQA
 
-Architecture verified with rotary embeddings, grouped query attention, SwiGLU, and RMSNorm.
+Tokenizer verified (32k vocab, unigram SentencePiece)
 
 Environmental Impact
 
 Hardware Type: Consumer GPU/CPU
 
-Hours used: Few days of training
+Training Time: Several days (low resource)
 
 Cloud Provider: None (local)
 
-Compute Region: Local system
-
-Carbon Emitted: Low (small model size)
+Carbon Emitted: Minimal (small model)
 
-Technical Specifications [optional]
+Technical Specifications 
 Model Architecture and Objective
 
-Decoder-only transformer, 8 layers, SwiGLU, GQA, RoPE
+Decoder-only transformer
+
+RoPE embeddings, SwiGLU MLP, RMSNorm
+
+Grouped Query Attention
 
 Compute Infrastructure
 
-Hardware: ~7GB RAM device (tested on consumer GPU/CPU)
+Hardware: ~7 GB RAM system
 
 Software: PyTorch, SentencePiece
 
-Citation [optional]
+Citation 
 
 BibTeX:
 
@@ -198,25 +212,25 @@ BibTeX:
 
 
 APA:
-Nexizan Company. (2025). CoreX v0.1: Lightweight Transformer Language Model.
+Nexizan inc (2025). CoreX v0.1: Lightweight Transformer Language Model.
 
-Glossary [optional]
+Glossary
 
-RoPE: Rotary Position Embedding
+RoPE: Rotary Position Embeddings
 
 SwiGLU: Swish-Gated Linear Unit
 
-RMSNorm: Root Mean Square Normalization
+RMSNorm: Root Mean Square Norm
 
 GQA: Grouped Query Attention
 
-More Information [optional]
+More Information 
 
-CoreX is intended as a stepping stone toward future versions with larger parameter counts and better datasets.
+CoreX v0.1 is the first milestone in the CoreX series, focused on offline-first, privacy-respecting AI systems. Future versions aim for larger datasets, more parameters, and better reasoning ability.
 
-Model Card Authors [optional]
+Model Card Authors
 
-Nexizan Company CoreX Team
+Nexizan inc — CoreX Team
 
 Model Card Contact