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Model: haykgrigorian/TimeCapsuleLLM-v2-llama-1.2B Source: Original Platform
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LICENSE
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LICENSE
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MIT License
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Copyright (c) 2025 Hayk Grigorian
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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README.md
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README.md
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---
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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)
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config.json
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 0,
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"dtype": "bfloat16",
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"eos_token_id": 1,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 5504,
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"max_position_embeddings": 2048,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 16,
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"num_hidden_layers": 22,
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"num_key_value_heads": 8,
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"pad_token_id": 3,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"transformers_version": "4.57.3",
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"use_cache": false,
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"vocab_size": 32000
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}
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configuration.json
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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generation_config.json
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}
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model.safetensors
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special_tokens_map.json
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tokenizer.json
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tokenizer_config.json
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training_args.bin
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training_args.bin
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