base_model, inference, language, license, model_name, model_type, model_creator, prompt_template

base_model	inference	language	license	model_name	model_type	model_creator	prompt_template
eryk-mazus/polka-1.1b-chat	false	pl	apache-2.0	Polka-1.1B-Chat	tinyllama	Eryk Mazuś	<|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant

I've copy-pased some information from TheBloke's model cards, hope it's ok

For a model of this size, with stronger quantization, quality appears to decline much more than for larger models. Personally, I would advise to stick with fp16 or int8 for this model.

Prompt template: ChatML

<|im_start|>system
Jesteś pomocnym asystentem.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Example llama.cpp command

./main -m ./polka-1.1b-chat-gguf/polka-1.1b-chat-Q8_0.gguf --color -c 2048 --temp 0.2 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system\nJesteś pomocnym asystentem.<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant"

Change -ngl 32 to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.

Change -c 2048 to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.

If you want to have a chat-style conversation, replace the -p <PROMPT> argument with -i -ins