---
base_model: allenai/OLMo-2-0425-1B-Instruct
datasets:
- allenai/RLVR-MATH
language:
- en
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
tags:
- llama-cpp
- gguf-my-repo
---

# Triangle104/OLMo-2-0425-1B-Instruct-Q4_K_S-GGUF
This model was converted to GGUF format from [`allenai/OLMo-2-0425-1B-Instruct`](https://huggingface.co/allenai/OLMo-2-0425-1B-Instruct) 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/allenai/OLMo-2-0425-1B-Instruct) for more details on the model.

---
OLMo 2 1B Instruct April 2025 is post-trained variant of the allenai/OLMo-2-0425-1B-RLVR1 model, which has undergone supervised finetuning on an OLMo-specific variant of the Tülu 3 dataset, further DPO training on this dataset, and final RLVR training on this dataset.
Tülu 3 is designed for state-of-the-art performance on a diversity of 
tasks in addition to chat, such as MATH, GSM8K, and IFEval.
Check out the OLMo 2 paper or Tülu 3 paper for more details!


OLMo is a series of Open Language Models designed to enable the science of language models. 
These models are trained on the Dolma dataset. We are releasing all code, checkpoints, logs, and associated training details.

---
## 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/OLMo-2-0425-1B-Instruct-Q4_K_S-GGUF --hf-file olmo-2-0425-1b-instruct-q4_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/OLMo-2-0425-1B-Instruct-Q4_K_S-GGUF --hf-file olmo-2-0425-1b-instruct-q4_k_s.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/OLMo-2-0425-1B-Instruct-Q4_K_S-GGUF --hf-file olmo-2-0425-1b-instruct-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/OLMo-2-0425-1B-Instruct-Q4_K_S-GGUF --hf-file olmo-2-0425-1b-instruct-q4_k_s.gguf -c 2048
```