How to use from
vLLM
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kerzgrr/Monostich-100M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kerzgrr/Monostich-100M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'
Use Docker
docker model run hf.co/kerzgrr/Monostich-100M:F16
Quick Links

Monostich GGUF

GGUF format of Monostich 100M for use with llama.cpp and compatible tools.

File Description
monostich-f16.gguf FP16 (full precision)

Download

# All GGUF files
huggingface-cli download kerzgrr/Monostich-100M --include "*.gguf" --local-dir .

# Or a specific file
huggingface-cli download kerzgrr/Monostich-100M monostich-f16.gguf --local-dir .

Direct URL (for wget/curl):

https://huggingface.co/kerzgrr/Monostich-100M/resolve/main/monostich-f16.gguf

Run with llama.cpp

1. Build llama.cpp

git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
cmake -B build -DGGML_CUDA=ON   # optional: GPU
cmake --build build --config Release

2. Interactive chat

./build/bin/llama-cli -m monostich-f16.gguf \
  -c 1024 \
  --temp 0.28 \
  --top-p 0.9 \
  -i
  • -c 1024 — context length (max 1024)
  • --temp 0.28 — sampling temperature
  • --top-p 0.9 — nucleus sampling
  • -i — interactive mode

3. Single prompt (no chat UI)

./build/bin/llama-cli -m monostich-f16.gguf \
  -p "Hello, how are you?" \
  -n 128 \
  -c 1024 \
  --temp 0.28
  • -p — prompt
  • -n — max new tokens

4. Chat template (instruction / assistant style)

For instruction-tuned behavior, use the Llama-3-style chat format:

<|begin_of_text|><|start_header_id|>user<|end_header_id|>

Your question here<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Example prompt:

./build/bin/llama-cli -m monostich-f16.gguf \
  -p "<|begin_of_text|><|start_header_id|>user<|end_header_id|>

What is 2+2?<|eot_id|><|start_header_id|>assistant<|end_header_id|>

" \
  -n 128 -c 1024 --temp 0.28

Run with llama-cpp-python (Python)

pip install llama-cpp-python
from llama_cpp import Llama

llm = Llama(model_path="monostich-f16.gguf", n_ctx=1024)

out = llm(
    "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nHello<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
    max_tokens=128,
    temperature=0.28,
    top_p=0.9,
)
print(out["choices"][0]["text"])

Model card

For architecture, training, and license details, see the main model card in this repo or kerzgrr/Monostich-100M.

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GGUF
Model size
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Architecture
llama
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