README.md

---
license: creativeml-openrail-m
datasets:
- mlabonne/lmsys-arena-human-preference-55k-sharegpt
language:
- en
base_model: prithivMLmods/Llama-Sentient-3.2-3B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- Llama
- Llama-Cpp
- Llama3.2
- Instruct
- 3B
- bin
- Sentient
- NLU
- llama-cpp
- gguf-my-repo
---

# Triangle104/Llama-Sentient-3.2-3B-Instruct-Q5_K_M-GGUF
This model was converted to GGUF format from [`prithivMLmods/Llama-Sentient-3.2-3B-Instruct`](https://huggingface.co/prithivMLmods/Llama-Sentient-3.2-3B-Instruct) 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/prithivMLmods/Llama-Sentient-3.2-3B-Instruct) for more details on the model.

---
Model details:
-
The Llama-Sentient-3.2-3B-Instruct model is a fine-tuned version of the Llama-3.2-3B-Instruct model, optimized for text generation tasks, particularly where instruction-following abilities are critical. This model is trained on the mlabonne/lmsys-arena-human-preference-55k-sharegpt
 dataset, which enhances its performance in conversational and advisory 
contexts, making it suitable for a wide range of applications.
Key Use Cases:
	



Conversational AI: Engage in intelligent dialogue, 
offering coherent responses and following instructions, useful for 
customer support and virtual assistants.
Text Generation: Generate high-quality, 
contextually appropriate content such as articles, summaries, 
explanations, and other forms of written communication based on user 
prompts.
Instruction Following: Follow specific instructions
 with accuracy, making it ideal for tasks that require structured 
guidance, such as technical troubleshooting or educational assistance.


The model uses a PyTorch-based architecture and includes a range of necessary files such as configuration files, tokenizer files, and model weight files for deployment.



	
		
	

		Intended Applications:
	



Chatbots for virtual assistance, customer support, or as personal digital assistants.
Content Creation Tools, aiding in the generation of written materials, blog posts, or automated responses based on user inputs.
Educational and Training Systems, providing explanations and guided learning experiences in various domains.
Human-AI Interaction platforms, where the model can follow user instructions to provide personalized assistance or perform specific tasks.


With its strong foundation in instruction-following and conversational contexts, the Llama-Sentient-3.2-3B-Instruct model offers versatile applications for both general and specialized domains.

---
## 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/Llama-Sentient-3.2-3B-Instruct-Q5_K_M-GGUF --hf-file llama-sentient-3.2-3b-instruct-q5_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Llama-Sentient-3.2-3B-Instruct-Q5_K_M-GGUF --hf-file llama-sentient-3.2-3b-instruct-q5_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/Llama-Sentient-3.2-3B-Instruct-Q5_K_M-GGUF --hf-file llama-sentient-3.2-3b-instruct-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Llama-Sentient-3.2-3B-Instruct-Q5_K_M-GGUF --hf-file llama-sentient-3.2-3b-instruct-q5_k_m.gguf -c 2048
```