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---
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
---
[](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.
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