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---
base_model: Devarui379/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated
license: cc-by-4.0
language:
- en
pipeline_tag: text-generation
library_name: transformers
tags:
- llama-cpp
- gguf-my-repo
---
# Triangle104/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-Q5_K_S-GGUF
This model was converted to GGUF format from [`Devarui379/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated`](https://huggingface.co/Devarui379/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated) 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/Devarui379/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated) for more details on the model.
---
Model details:
-
Small but Smart
Fine-Tuned on Vast dataset of Conversations
Able to Generate Human like text with high performance within its size.
It is Very Versatile when compared for it's size and Parameters and offers capability almost as good as Llama 3.1 8B Instruct
Feel free to Check it out!!
[This model was trained for 5hrs on GPU T4 15gb vram]
Developed by: Meta AI
Fine-Tuned by: Devarui379
Model type: Transformers
Language(s) (NLP): English
License: cc-by-4.0
Model Sources [optional]
base model:meta-llama/Llama-3.2-3B-Instruct
Repository: Devarui379/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated
Demo: Use LM Studio with the Quantized version
Uses
Use desired System prompt when using in LM Studio The optimal chat template seems to be Jinja but feel free to test it out as you want!
Technical Specifications
Model Architecture and Objective
Llama 3.2
Hardware
NVIDIA TESLA T4 GPU 15GB VRAM
---
## 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/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-Q5_K_S-GGUF --hf-file versatillama-llama-3.2-3b-instruct-abliterated-q5_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-Q5_K_S-GGUF --hf-file versatillama-llama-3.2-3b-instruct-abliterated-q5_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/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-Q5_K_S-GGUF --hf-file versatillama-llama-3.2-3b-instruct-abliterated-q5_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/VersatiLlama-Llama-3.2-3B-Instruct-Abliterated-Q5_K_S-GGUF --hf-file versatillama-llama-3.2-3b-instruct-abliterated-q5_k_s.gguf -c 2048
```
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