---
base_model: allura-org/MS3.2-24b-Angel
library_name: transformers
tags:
- axolotl
- unsloth
- roleplay
- conversational
- llama-cpp
- gguf-my-repo
datasets:
- PygmalionAI/PIPPA
- Alfitaria/nemotron-ultra-reasoning-synthkink
- PocketDoc/Dans-Prosemaxx-Gutenberg
- FreedomIntelligence/Medical-R1-Distill-Data
- cognitivecomputations/SystemChat-2.0
- allenai/tulu-3-sft-personas-instruction-following
- kalomaze/Opus_Instruct_25k
- simplescaling/s1K-claude-3-7-sonnet
- ai2-adapt-dev/flan_v2_converted
- grimulkan/theory-of-mind
- grimulkan/physical-reasoning
- nvidia/HelpSteer3
- nbeerbower/gutenberg2-dpo
- nbeerbower/gutenberg-moderne-dpo
- nbeerbower/Purpura-DPO
- antiven0m/physical-reasoning-dpo
- allenai/tulu-3-IF-augmented-on-policy-70b
- allenai/href
---

# Triangle104/MS3.2-24b-Angel-Q8_0-GGUF
This model was converted to GGUF format from [`allura-org/MS3.2-24b-Angel`](https://huggingface.co/allura-org/MS3.2-24b-Angel) 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/allura-org/MS3.2-24b-Angel) for more details on the model.

---
MS3.2-24b-Angel is a model finetuned from Mistral Small 3.2 for roleplaying, storywriting, and differently-flavored general instruct usecases.

Testing revealed strong prose and character portrayal for its class, rivalling the preferred 72B models of some testers.

---
## 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/MS3.2-24b-Angel-Q8_0-GGUF --hf-file ms3.2-24b-angel-q8_0.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/MS3.2-24b-Angel-Q8_0-GGUF --hf-file ms3.2-24b-angel-q8_0.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/MS3.2-24b-Angel-Q8_0-GGUF --hf-file ms3.2-24b-angel-q8_0.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/MS3.2-24b-Angel-Q8_0-GGUF --hf-file ms3.2-24b-angel-q8_0.gguf -c 2048
```