---
license: cc-by-nc-sa-4.0
language:
- en
- zh
base_model: Krystalan/DRT-o1-14B
tags:
- machine tranlsation
- O1-like model
- Chat
- llama-cpp
- gguf-my-repo
pipeline_tag: text-generation
---

# Triangle104/DRT-o1-14B-Q5_K_S-GGUF
This model was converted to GGUF format from [`Krystalan/DRT-o1-14B`](https://huggingface.co./Krystalan/DRT-o1-14B) 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./Krystalan/DRT-o1-14B) for more details on the model.

---
Model details:
-
In this work, we introduce DRT-o1, an attempt to bring the success of
 long thought reasoning to neural machine translation (MT). To this end,


🌟 We mine English sentences with similes or metaphors from existing
 literature books, which are suitable for translation via long thought.
🌟 We propose a designed multi-agent framework with three agents 
(i.e., a translator, an advisor and an evaluator) to synthesize the MT 
samples with long thought. There are 22,264 synthesized samples in 
total.
🌟 We train DRT-o1-8B, DRT-o1-7B and DRT-o1-14B using 
Llama-3.1-8B-Instruct, Qwen2.5-7B-Instruct and Qwen2.5-14B-Instruct as 
backbones.




Our goal is not to achieve competitive performance with OpenAI’s O1 
in neural machine translation (MT). Instead, we explore technical routes
 to bring the success of long thought to MT. To this end, we introduce 
DRT-o1, a byproduct of our exploration, and we hope it could facilitate the corresponding research in this direction.

---
## 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/DRT-o1-14B-Q5_K_S-GGUF --hf-file drt-o1-14b-q5_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/DRT-o1-14B-Q5_K_S-GGUF --hf-file drt-o1-14b-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/DRT-o1-14B-Q5_K_S-GGUF --hf-file drt-o1-14b-q5_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/DRT-o1-14B-Q5_K_S-GGUF --hf-file drt-o1-14b-q5_k_s.gguf -c 2048
```