Instructions to use haykgrigorian/TimeCapsuleLLM-v2-llama-1.2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use haykgrigorian/TimeCapsuleLLM-v2-llama-1.2B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="haykgrigorian/TimeCapsuleLLM-v2-llama-1.2B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("haykgrigorian/TimeCapsuleLLM-v2-llama-1.2B")
model = AutoModelForCausalLM.from_pretrained("haykgrigorian/TimeCapsuleLLM-v2-llama-1.2B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use haykgrigorian/TimeCapsuleLLM-v2-llama-1.2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "haykgrigorian/TimeCapsuleLLM-v2-llama-1.2B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "haykgrigorian/TimeCapsuleLLM-v2-llama-1.2B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/haykgrigorian/TimeCapsuleLLM-v2-llama-1.2B

SGLang

How to use haykgrigorian/TimeCapsuleLLM-v2-llama-1.2B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "haykgrigorian/TimeCapsuleLLM-v2-llama-1.2B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "haykgrigorian/TimeCapsuleLLM-v2-llama-1.2B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "haykgrigorian/TimeCapsuleLLM-v2-llama-1.2B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "haykgrigorian/TimeCapsuleLLM-v2-llama-1.2B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use haykgrigorian/TimeCapsuleLLM-v2-llama-1.2B with Docker Model Runner:
```
docker model run hf.co/haykgrigorian/TimeCapsuleLLM-v2-llama-1.2B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

haykgrigorian/TimeCapsuleLLM-v2-London-1800-1875: Llama-Architecture 1.2B Model

Model Overview

v2 model, trained from scratch on 112GB of 1800-1875 london texts using a Llama-based Casual Language Model.

Detail	Value
Model Architecture	LlamaForCausalLM (Decoder-Only Transformer)
Parameter Count	~1.22B
Training Type	Trained from Scratch (Random Initialization)
Tokenizer	Custom BPE, Vocab Size 32,000
Sequence Length	2048 tokens
Attention Type	Grouped Query Attention (GQA)

Configuration Details

This model is a custom size and configuration based on Llama:

Parameter	Value
Number of Layers	22
Hidden Size (d)	2048
Intermediate Size ($\text{d}_{\text{ff}}$)	5504
Attention Heads	16 (Query) / 8 (Key/Value)
Activation Function	SiLU (`silu`)
Normalization	RMS Norm (`rms_norm_eps`: 1e-06)
Position Embeddings	Rotary Positional Embeddings (RoPE)

Training Info

This model was trained for 182,000 steps, about 0.5 epochs.

Training Metrics:

Final Training Loss: 3.3951

Start Training Loss: 10.7932

Training Steps: 182,000

Epochs: 0.4997

Gradient Norm Stability: Consistently stable between 0.50 and 0.60 in later stages.

Training time: 117 hours 51 minutes

Cost

This model was trained on an H100 SXM from RunPod

Total: $340.97

How to Load and Run the Model

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