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README.md

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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
+
+---
+
+[![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.