---
base_model: Hastagaras/Zabuza-8B-Llama-3.1
library_name: transformers
license: llama3.1
pipeline_tag: text-generation
tags:
- mergekit
- merge
- not-for-all-audiences
- llama-cpp
- gguf-my-repo
---

# Triangle104/Zabuza-8B-Llama-3.1-Q6_K-GGUF
This model was converted to GGUF format from [`Hastagaras/Zabuza-8B-Llama-3.1`](https://huggingface.co/Hastagaras/Zabuza-8B-Llama-3.1) 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/Hastagaras/Zabuza-8B-Llama-3.1) for more details on the model.

---
Model details:
-
This model is a combination of merge, abliteration technique (using baukit) and finetuning.

The base model is arcee-ai/Llama-3.1-SuperNova-Lite, which underwent abliteration to reduce model refusals.

Next, I finetuned the abliterated SuperNova-Lite with 10K diverse examples such as:

    Claude and Gemini Instruction/RP (15k sloppy examples were removed!, but some may have slipped through.)
    Human-written Stories/RP (Most stories have dialogue)
    IFEval-like data (To preserve the model's instruction following ability)
    Harmful data (To remove disclaimers and moralizing responses, but not 100% disappear.)
    My sarcastic and rude AI assistant data (Just for my personal satisfaction)

Lastly, I merged the model using TIES, inspired by this MERGE by Joseph717171.

Chat Template
-
Llama 3.1 Instruct
-
<|start_header_id|>{role}<|end_header_id|>

{message}<|eot_id|><|start_header_id|>{role}<|end_header_id|>

{message}<|eot_id|>

System messages for role-playing should be very detailed if you don't want dry responses.

Configuration
-
This is a merge of pre-trained language models created using mergekit.

The following YAML configuration was used to produce this model:

models:
  - model: Hastagaras/snovalite-baukit-6-14.FT-L5-7.13-22.27-31
    parameters:
      weight: 1
      density: 1

  - model: Hastagaras/snovalite-baukit-6-14.FT-L5-7.13-22.27-31
    parameters:
      weight: 1
      density: 1

merge_method: ties
base_model: meta-llama/Llama-3.1-8B
parameters:
  density: 1
  normalize: true
  int8_mask: true
dtype: bfloat16

---
## 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/Zabuza-8B-Llama-3.1-Q6_K-GGUF --hf-file zabuza-8b-llama-3.1-q6_k.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Zabuza-8B-Llama-3.1-Q6_K-GGUF --hf-file zabuza-8b-llama-3.1-q6_k.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/Zabuza-8B-Llama-3.1-Q6_K-GGUF --hf-file zabuza-8b-llama-3.1-q6_k.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Zabuza-8B-Llama-3.1-Q6_K-GGUF --hf-file zabuza-8b-llama-3.1-q6_k.gguf -c 2048
```