---
base_model: open-thoughts/OpenThinker-32B
datasets:
- open-thoughts/open-thoughts-114k
library_name: transformers
license: apache-2.0
tags:
- llama-factory
- full
- generated_from_trainer
- llama-cpp
- gguf-my-repo
model-index:
- name: OpenThinker-32B
  results: []
---

# Triangle104/OpenThinker-32B-Q5_K_S-GGUF
This model was converted to GGUF format from [`open-thoughts/OpenThinker-32B`](https://huggingface.co/open-thoughts/OpenThinker-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/open-thoughts/OpenThinker-32B) for more details on the model.

---
This model is a fine-tuned version of Qwen/Qwen2.5-32B-Instruct on the OpenThoughts-114k dataset.

The dataset is derived by distilling DeepSeek-R1 using the data pipeline available on github. More info about the dataset can be found on the dataset card at OpenThoughts-114k dataset.

Intended uses & limitations
-
Apache 2.0 License

Training procedure
-
We finetune Qwen2.5-32B-Instruct on OpenThoughts-114k for 3 epochs with a 16k context length using LlamaFactory. Our full training configuration is provided in our repository. Training the 32B model on OpenThoughts-114k was done on AWS SageMaker with 8xH100 P5 nodes. On 4 nodes, this took around 90 hours. Meanwhile, for training on OpenThoughts-Unverified-173k, we used 96 nodes of 4xA100 (64 GB per GPU), training took 30 hours, spending 11,520 A100 hours on the Leonardo Supercomputer.

---
## 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/OpenThinker-32B-Q5_K_S-GGUF --hf-file openthinker-32b-q5_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/OpenThinker-32B-Q5_K_S-GGUF --hf-file openthinker-32b-q5_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/OpenThinker-32B-Q5_K_S-GGUF --hf-file openthinker-32b-q5_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/OpenThinker-32B-Q5_K_S-GGUF --hf-file openthinker-32b-q5_k_s.gguf -c 2048
```