---
base_model: LGAI-EXAONE/EXAONE-Deep-32B
base_model_relation: finetune
license: other
license_name: exaone
license_link: LICENSE
language:
- en
- ko
tags:
- lg-ai
- exaone
- exaone-deep
- llama-cpp
- gguf-my-repo
pipeline_tag: text-generation
library_name: transformers
---

# Triangle104/EXAONE-Deep-32B-Q4_K_M-GGUF
This model was converted to GGUF format from [`LGAI-EXAONE/EXAONE-Deep-32B`](https://huggingface.co/LGAI-EXAONE/EXAONE-Deep-32B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/LGAI-EXAONE/EXAONE-Deep-32B) for more details on the model.

---
We introduce EXAONE Deep, which exhibits superior capabilities in various reasoning tasks including math and coding benchmarks, ranging from 2.4B to 32B parameters developed and released by LG AI Research. Evaluation results show that 1) EXAONE Deep 2.4B outperforms other models of comparable size, 2) EXAONE Deep 7.8B outperforms not only open-weight models of comparable scale but also a proprietary reasoning model OpenAI o1-mini, and 3) EXAONE Deep 32B demonstrates competitive performance against leading open-weight models.

---
## 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 Triangle104/EXAONE-Deep-32B-Q4_K_M-GGUF --hf-file exaone-deep-32b-q4_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/EXAONE-Deep-32B-Q4_K_M-GGUF --hf-file exaone-deep-32b-q4_k_m.gguf -c 2048
```

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 Triangle104/EXAONE-Deep-32B-Q4_K_M-GGUF --hf-file exaone-deep-32b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/EXAONE-Deep-32B-Q4_K_M-GGUF --hf-file exaone-deep-32b-q4_k_m.gguf -c 2048
```