75 lines
2.0 KiB
Markdown
75 lines
2.0 KiB
Markdown
---
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license: mit
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- llama
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- historical
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- causal-lm
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datasets:
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- postgrammar/london-llm-1800
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---
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# haykgrigorian/TimeCapsuleLLM-v2-London-1800-1875: Llama-Architecture 1.2B Model
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## Model Overview
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**v2** model, trained from scratch on 112GB of 1800-1875 london texts using a Llama-based Casual Language Model.
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| Detail | Value |
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| :--- | :--- |
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| **Model Architecture** | LlamaForCausalLM (Decoder-Only Transformer) |
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| **Parameter Count** | **~1.22B** |
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| **Training Type** | Trained **from Scratch** (Random Initialization) |
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| **Tokenizer** | Custom BPE, Vocab Size 32,000 |
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| **Sequence Length** | 2048 tokens |
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| **Attention Type** | Grouped Query Attention (GQA) |
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## Configuration Details
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This model is a custom size and configuration based on Llama:
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| Parameter | Value |
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| :--- | :--- |
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| **Number of Layers** | 22 |
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| **Hidden Size (d)** | 2048 |
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| **Intermediate Size ($\text{d}_{\text{ff}}$)** | 5504 |
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| **Attention Heads** | 16 (Query) / 8 (Key/Value) |
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| **Activation Function** | SiLU (`silu`) |
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| **Normalization** | RMS Norm (`rms_norm_eps`: 1e-06) |
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| **Position Embeddings** | Rotary Positional Embeddings (RoPE) |
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## Training Info
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This model was trained for 182,000 steps, about 0.5 epochs.
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Training Metrics:
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Final Training Loss: 3.3951
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Start Training Loss: 10.7932
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Training Steps: 182,000
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Epochs: 0.4997
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Gradient Norm Stability: Consistently stable between 0.50 and 0.60 in later stages.
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Training time: 117 hours 51 minutes
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### Cost
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This model was trained on an H100 SXM from RunPod
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Total: $340.97
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### How to Load and Run the Model
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Install all the files locally in a folder and run the test script. You will have to make some adjustments in the run script like updating the config/file path and test prompts
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### Test script
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A run file for testing and evaluating this model is available on the main project repository:
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* **Test Script Link:** [run_v2.py on GitHub](https://github.com/haykgrigo3/TimeCapsuleLLM/blob/main/london_1800_1875_v2/run_v2.py) |