---
license: apache-2.0
tags:
- alignment-handbook
- generated_from_trainer
- llama-cpp
- gguf-my-repo
language:
- en
metrics:
- accuracy
pipeline_tag: text-generation
base_model: THU-KEG/Mistral-Crab-DPO
---

# Triangle104/Mistral-Crab-DPO-Q4_K_M-GGUF
This model was converted to GGUF format from [`THU-KEG/Mistral-Crab-DPO`](https://huggingface.co/THU-KEG/Mistral-Crab-DPO) 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/THU-KEG/Mistral-Crab-DPO) for more details on the model.

---
Model details:
-

Large language models (LLMs) struggle to follow instructions with complex constraints in format, length, etc. Following the conventional instruction-tuning practice, previous works conduct post-training on complex instruction-response pairs generated by feeding complex instructions to advanced LLMs. However, even advanced LLMs cannot follow complex instructions well, thus limiting the quality of generated data. In this work, we find that existing datasets inherently contain implicit complex constraints and propose a novel data generation technique, constraint back-translation. Specifically, we take the high-quality instruction-response pairs in existing datasets and only adopt advanced LLMs to add complex constraints already met by the responses to the instructions, which naturally reduces costs and data noise. In the experiments, we adopt Llama3-70B-Instruct to back-translate constraints and create a high-quality complex instruction-response dataset, named CRAB. We present that post-training on CRAB improves multiple backbone LLMs' complex instruction-following ability, evaluated on extensive instruction-following benchmarks. We further find that constraint back-translation also serves as a useful auxiliary training objective in post-training.

    📖 Paper: Constraint Back-translation Improves Complex Instruction Following of Large Language Models

    🦀 Github: THU/Crab

Model Description
-
    Developed by: Yunjia Qi, Hao Peng, Xiaozhi Wang, Bin Xu, Lei Hou, Juanzi Li
    Model type: Text Generation
    Language(s) (NLP): English
    Finetuned from model [optional]: Mistral-7B-v0.3

---
## 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/Mistral-Crab-DPO-Q4_K_M-GGUF --hf-file mistral-crab-dpo-q4_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Mistral-Crab-DPO-Q4_K_M-GGUF --hf-file mistral-crab-dpo-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/Mistral-Crab-DPO-Q4_K_M-GGUF --hf-file mistral-crab-dpo-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Mistral-Crab-DPO-Q4_K_M-GGUF --hf-file mistral-crab-dpo-q4_k_m.gguf -c 2048
```