| --- |
| license: apache-2.0 |
| task_categories: |
| - text-generation |
| language: |
| - en |
| - uz |
| - ru |
| tags: |
| - causal-lm |
| - foundational |
| - custom-architectures |
| - pytorch |
| - d5-series |
| - flash-attention |
| - multi-lingual |
| --- |
| |
| # LLM_D5 Model Card |
| |
| ## Model Description |
| |
| `LLM_D5` is an experimental foundational autoregressive large language model representing the fifth generation iteration (D5) of custom architectural model training setups. Built entirely from scratch via the orchestration engines provided in the companion [firdavsus/LLM_D5 GitHub repository](https://github.com/firdavsus/LLM_D5), this framework is tailored for ultra-low latency inference, efficient localized deployment, and highly specialized bilingual or trilingual applications. |
|
|
| The D5 iteration introduces deeper structural optimizations over previous series runs, adapting advanced attention pooling mechanisms, robust layer dynamics, and refined vocab boundaries specifically tuned for clean multi-lingual handling across **English (`en`)**, **Uzbek (`uz`)**, and **Russian (`ru`)**. |
|
|
| ### Model Features & Specifications |
| - **Model Series:** D5 Iteration Branch |
| - **Task:** Causal Language Modeling (`text-generation`) |
| - **Core Architecture:** Autoregressive Transformer with decoupled hidden representations, Pre-Layer RMSNorm bounding, and Rotary Position Embeddings (RoPE). |
| - **Attention Protocol:** Enhanced Multi-Head / Grouped-Query Attention with native FlashAttention-2 speedup support. |
|
|
| --- |
|
|
| ## Intended Uses & Limitations |
|
|
| ### Target Applications |
| - **Multilingual Edge Computing:** Lightweight downstream text generation, text structure tokenization, or conversational tasks on isolated GPU workstations. |
| - **Architectural Scaling Research:** Benchmarking sequential state handling, context growth decay, and layer stability profiles across individual training epochs. |
| - **Cross-Lingual Adaptation:** Easily adaptable for specialized sequence classification, instruction following, or fine-tuning across Central Asian language sets. |
|
|
| ### Limitations |
| - **Zero-Shot Complexity:** Due to the custom foundational scope, raw checkpoints may require specific chat templating or fine-tuning wrappers to cleanly execute complex multi-step reasoning or mathematical logical pathways without structural deviation. |
| - **Tokenizer Bounds:** Sequence token distribution is structurally locked to the vocabulary configuration generated in the D5 preprocessing modules. |
|
|
| --- |
|
|
| ## Quickstart Inference |
|
|
| You can initialize and extract representations directly from the D5 architecture using PyTorch components provided in the project source repository. |
|
|
| ```python |
| import torch |
| from model import Transformer, ModelArgs # Imported from your firdavsus/LLM_D5 codebase |
| from tokenizer import Tokenizer |
| |
| # 1. Initialize architectural shape configurations |
| args = ModelArgs( |
| dim=2048, |
| n_layers=32, |
| n_heads=32, |
| vocab_size=50257, |
| max_seq_len=4096 |
| ) |
| |
| # 2. Allocate space and load internal network weights |
| device = "cuda" if torch.cuda.is_available() else "cpu" |
| model = Transformer(args).to(device) |
| |
| checkpoint = torch.load("path_to_d5_checkpoint.pt", map_location=device) |
| model.load_state_dict(checkpoint["model"]) |
| model.eval() |
| |
| print("LLM_D5 pipeline initialized and ready for sequence generation loops.") |
