---
base_model: nbeerbower/Dumpling-Qwen2.5-32B-v2
datasets:
- nbeerbower/GreatFirewall-DPO
- nbeerbower/Schule-DPO
- nbeerbower/Purpura-DPO
- nbeerbower/Arkhaios-DPO
- jondurbin/truthy-dpo-v0.1
- antiven0m/physical-reasoning-dpo
- flammenai/Date-DPO-NoAsterisks
- flammenai/Prude-Phi3-DPO
- Atsunori/HelpSteer2-DPO
- jondurbin/gutenberg-dpo-v0.1
- nbeerbower/gutenberg2-dpo
- nbeerbower/gutenberg-moderne-dpo
library_name: transformers
license: apache-2.0
tags:
- llama-cpp
- gguf-my-repo
---

# Triangle104/Dumpling-Qwen2.5-32B-v2-Q4_K_M-GGUF
This model was converted to GGUF format from [`nbeerbower/Dumpling-Qwen2.5-32B-v2`](https://huggingface.co/nbeerbower/Dumpling-Qwen2.5-32B-v2) 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/nbeerbower/Dumpling-Qwen2.5-32B-v2) for more details on the model.

---
[nbeerbower/Rombos-EVAGutenberg-TIES-Qwen2.5-32B](https://huggingface.co/nbeerbower/Rombos-EVAGutenberg-TIES-Qwen2.5-32B) finetuned on: 
   
* [nbeerbower/GreatFirewall-DPO](https://huggingface.co/datasets/nbeerbower/GreatFirewall-DPO)

* [nbeerbower/Schule-DPO](https://huggingface.co/datasets/nbeerbower/Schule-DPO)

* [nbeerbower/Purpura-DPO](https://huggingface.co/datasets/nbeerbower/Purpura-DPO)

* [nbeerbower/Arkhaios-DPO](https://huggingface.co/datasets/nbeerbower/Arkhaios-DPO)

* [jondurbin/truthy-dpo-v0.1](https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1)

* [antiven0m/physical-reasoning-dpo](https://huggingface.co/datasets/antiven0m/physical-reasoning-dpo)

* [flammenai/Date-DPO-NoAsterisks](https://huggingface.co/datasets/flammenai/Date-DPO-NoAsterisks)

* [flammenai/Prude-Phi3-DPO](https://huggingface.co/datasets/flammenai/Prude-Phi3-DPO)

* [Atsunori/HelpSteer2-DPO](https://huggingface.co/datasets/Atsunori/HelpSteer2-DPO)

* [jondurbin/gutenberg-dpo-v0.1](https://huggingface.co/datasets/jondurbin/gutenberg-dpo-v0.1)

* [nbeerbower/gutenberg2-dpo](https://huggingface.co/datasets/nbeerbower/gutenberg2-dpo)

* [nbeerbower/gutenberg-moderne-dpo](https://huggingface.co/datasets/nbeerbower/gutenberg-moderne-dpo).

### Method

[QLoRA ORPO tuned](https://mlabonne.github.io/blog/posts/2024-04-19_Fine_tune_Llama_3_with_ORPO.html) with 8x A100 for 2 epochs. Rank 64 LoRA, 2e-5 learning rate.

---
## 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/Dumpling-Qwen2.5-32B-v2-Q4_K_M-GGUF --hf-file dumpling-qwen2.5-32b-v2-q4_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Dumpling-Qwen2.5-32B-v2-Q4_K_M-GGUF --hf-file dumpling-qwen2.5-32b-v2-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/Dumpling-Qwen2.5-32B-v2-Q4_K_M-GGUF --hf-file dumpling-qwen2.5-32b-v2-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Dumpling-Qwen2.5-32B-v2-Q4_K_M-GGUF --hf-file dumpling-qwen2.5-32b-v2-q4_k_m.gguf -c 2048
```