---
base_model: Delta-Vector/Rei-V2-12B
datasets:
- PocketDoc/Dans-Personamaxx-Logs
- anthracite-org/kalo-opus-instruct-22k-no-refusal
- lodrick-the-lafted/kalo-opus-instruct-3k-filtered
- anthracite-org/nopm_claude_writing_fixed
- anthracite-org/kalo_opus_misc_240827
- anthracite-org/kalo_misc_part2
- NewEden/Claude-Instruct-5K
- NewEden/Claude-Instruct-2.7K
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- roleplay
- finetune
- mistral
- magnum
- claude
- story-writing
- llama-cpp
- gguf-my-repo
---

# Triangle104/Rei-V2-12B-Q4_K_S-GGUF
This model was converted to GGUF format from [`Delta-Vector/Rei-V2-12B`](https://huggingface.co/Delta-Vector/Rei-V2-12B) 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/Delta-Vector/Rei-V2-12B) for more details on the model.

---
Originally conceived as an experiment to test the effects of gradient clipping, this model was exceptionally well-received by early testers, prompting its official release.

Fine-tuned on top of Mistral-Nemo-Instruct (ChatML'ified), Rei-12B is designed to replicate the exquisite prose quality of Claude 3 models, particularly Sonnet and Opus, using a prototype Magnum V5 datamix.

---
## 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/Rei-V2-12B-Q4_K_S-GGUF --hf-file rei-v2-12b-q4_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Rei-V2-12B-Q4_K_S-GGUF --hf-file rei-v2-12b-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/Rei-V2-12B-Q4_K_S-GGUF --hf-file rei-v2-12b-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Rei-V2-12B-Q4_K_S-GGUF --hf-file rei-v2-12b-q4_k_s.gguf -c 2048
```