Instructions to use haykgrigorian/TimeCapsuleLLM-v2mini-eval1-llama-300M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use haykgrigorian/TimeCapsuleLLM-v2mini-eval1-llama-300M with Transformers:

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

pipe = pipeline("text-generation", model="haykgrigorian/TimeCapsuleLLM-v2mini-eval1-llama-300M")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("haykgrigorian/TimeCapsuleLLM-v2mini-eval1-llama-300M")
model = AutoModelForCausalLM.from_pretrained("haykgrigorian/TimeCapsuleLLM-v2mini-eval1-llama-300M")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use haykgrigorian/TimeCapsuleLLM-v2mini-eval1-llama-300M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "haykgrigorian/TimeCapsuleLLM-v2mini-eval1-llama-300M"
# 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-v2mini-eval1-llama-300M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/haykgrigorian/TimeCapsuleLLM-v2mini-eval1-llama-300M

SGLang

How to use haykgrigorian/TimeCapsuleLLM-v2mini-eval1-llama-300M 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-v2mini-eval1-llama-300M" \
    --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-v2mini-eval1-llama-300M",
		"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-v2mini-eval1-llama-300M" \
        --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-v2mini-eval1-llama-300M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use haykgrigorian/TimeCapsuleLLM-v2mini-eval1-llama-300M with Docker Model Runner:
```
docker model run hf.co/haykgrigorian/TimeCapsuleLLM-v2mini-eval1-llama-300M
```

Dataset question

by Jonnob - opened Dec 16, 2025

Discussion

Jonnob

Dec 16, 2025

Hi What dataset was used to create this model? Is it publicly available?

haykgrigorian

Owner Dec 18, 2025

You can find it here: https://huggingface.co/datasets/haykgrigorian/TimeCapsuleLLM-London-1800-1875-v2-15GB, thanks for checking out the project!

haykgrigorian changed discussion status to closed Dec 18, 2025

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