---
language:
- it
- en
license: apache-2.0
tags:
- text-generation
- causal-lm
- bilingual
- italian
- english
- small-language-model
- trained-from-scratch
- quark
- llama-cpp
- gguf-my-repo
library_name: transformers
pipeline_tag: text-generation
base_model: ThingAI/Quark-270m-Base
---

# usermma/Quark-270m-Base-i1-GGUF
This model was converted to GGUF format from [`ThingAI/Quark-270m-Base`](https://huggingface.co/ThingAI/Quark-270m-Base) using llama.cpp.
Refer to the [original model card](https://huggingface.co/ThingAI/Quark-270m-Base) for more details on the model.

## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo usermma/Quark-270m-Base-i1-GGUF --hf-file quark-270m-base-iq1_s-imat.gguf --temp 0.8 --top-k 50 -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo usermma/Quark-270m-Base-i1-GGUF --hf-file quark-270m-base-iq1_s-imat.gguf -c 2048 --temp 0.9 --top-p 0.7
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```

Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```

Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo usermma/Quark-270m-Base-i1-GGUF --hf-file quark-270m-base-iq1_s-imat.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo usermma/Quark-270m-Base-i1-GGUF --hf-file quark-270m-base-iq1_s-imat.gguf -c 2048 --temp 1.2 --top-p 12
```