---

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

---

[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)


# QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF
This is quantized version of [prithivMLmods/Llama-Sentient-3.2-3B-Instruct](https://huggingface.co/prithivMLmods/Llama-Sentient-3.2-3B-Instruct) created using llama.cpp

# Original Model Card

## Llama-Sentient-3.2-3B-Instruct Modelfile

| File Name                               | Size         | Description                             | Upload Status  |
|-----------------------------------------|--------------|-----------------------------------------|----------------|
| `.gitattributes`                        | 1.57 kB      | Git attributes configuration file       | Uploaded       |
| `README.md`                             | 42 Bytes     | Initial commit README                  | Uploaded       |
| `config.json`                           | 1.04 kB      | Configuration file                      | Uploaded       |
| `generation_config.json`                | 248 Bytes    | Generation configuration file           | Uploaded       |
| `pytorch_model-00001-of-00002.bin`      | 4.97 GB      | PyTorch model file (part 1)             | Uploaded (LFS) |
| `pytorch_model-00002-of-00002.bin`      | 1.46 GB      | PyTorch model file (part 2)             | Uploaded (LFS) |
| `pytorch_model.bin.index.json`          | 21.2 kB      | Model index file                        | Uploaded       |
| `special_tokens_map.json`               | 477 Bytes    | Special tokens mapping                  | Uploaded       |
| `tokenizer.json`                        | 17.2 MB      | Tokenizer JSON file                     | Uploaded (LFS) |
| `tokenizer_config.json`                 | 57.4 kB      | Tokenizer configuration file            | Uploaded       |

| Model Type | Size | Context Length | Link |
|------------|------|----------------|------|
| GGUF | 3B | - | [🤗 Llama-Sentient-3.2-3B-Instruct-GGUF](https://huggingface.co/prithivMLmods/Llama-Sentient-3.2-3B-Instruct-GGUF) |

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:
1. **Conversational AI**: Engage in intelligent dialogue, offering coherent responses and following instructions, useful for customer support and virtual assistants.
2. **Text Generation**: Generate high-quality, contextually appropriate content such as articles, summaries, explanations, and other forms of written communication based on user prompts.
3. **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.