How to use from
vLLM
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nanite-Labs/nanites-medieval-e2b-chat-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nanite-Labs/nanites-medieval-e2b-chat-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'
Use Docker
docker model run hf.co/Nanite-Labs/nanites-medieval-e2b-chat-gguf:Q4_K_M
Quick Links

nanites-medieval-e2b-chat-GGUF

Q4_K_M quant of the merged nanites-medieval-e2b-chat model — a Gemma-4-E2B fine-tune that speaks as a literate person of late-medieval England. For Ollama / LM Studio / llama.cpp users who don't want the base-model + adapter merge step.

Requires a recent runtime (llama.cpp with Gemma 4 support, current Ollama) — older llama.cpp builds do not know the gemma4 architecture.

Adapter (train output) lives at Nanite-Labs/nanites-medieval-e2b-chat; this repo holds the merged + quantized GGUF for direct use.

Ollama

FROM ./medieval-Q4_K_M.gguf
ollama create nanites-medieval-e2b-chat-gguf -f Modelfile
ollama run nanites-medieval-e2b-chat-gguf

llama.cpp

llama-cli -m medieval-Q4_K_M.gguf -p "$(cat prompt.txt)" -n 200 --temp 0.7 -c 1024

Prompt format

Alpaca-style (### Instruction: / ### Input: / ### Response:). Example prompt:

You are a literate person of late-medieval England. Answer in 1-3 sentences with period vocabulary. A visitor asks: How fares your household this season?
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GGUF
Model size
5B params
Architecture
gemma4
Hardware compatibility
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