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--- |
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license: creativeml-openrail-m |
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datasets: |
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- mlabonne/lmsys-arena-human-preference-55k-sharegpt |
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language: |
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- en |
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base_model: |
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- meta-llama/Llama-3.2-3B-Instruct |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- Llama |
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- Llama-Cpp |
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- Llama3.2 |
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- Instruct |
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- 3B |
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- bin |
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- Sentient |
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--- |
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[](https://hf.co/QuantFactory) |
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# QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF |
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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 |
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# Original Model Card |
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## Llama-Sentient-3.2-3B-Instruct Modelfile |
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| File Name | Size | Description | Upload Status | |
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|-----------------------------------------|--------------|-----------------------------------------|----------------| |
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| `.gitattributes` | 1.57 kB | Git attributes configuration file | Uploaded | |
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| `README.md` | 42 Bytes | Initial commit README | Uploaded | |
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| `config.json` | 1.04 kB | Configuration file | Uploaded | |
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| `generation_config.json` | 248 Bytes | Generation configuration file | Uploaded | |
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| `pytorch_model-00001-of-00002.bin` | 4.97 GB | PyTorch model file (part 1) | Uploaded (LFS) | |
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| `pytorch_model-00002-of-00002.bin` | 1.46 GB | PyTorch model file (part 2) | Uploaded (LFS) | |
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| `pytorch_model.bin.index.json` | 21.2 kB | Model index file | Uploaded | |
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| `special_tokens_map.json` | 477 Bytes | Special tokens mapping | Uploaded | |
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| `tokenizer.json` | 17.2 MB | Tokenizer JSON file | Uploaded (LFS) | |
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| `tokenizer_config.json` | 57.4 kB | Tokenizer configuration file | Uploaded | |
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| Model Type | Size | Context Length | Link | |
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|------------|------|----------------|------| |
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| GGUF | 3B | - | [🤗 Llama-Sentient-3.2-3B-Instruct-GGUF](https://huggingface.co/prithivMLmods/Llama-Sentient-3.2-3B-Instruct-GGUF) | |
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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. |
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### Key Use Cases: |
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1. **Conversational AI**: Engage in intelligent dialogue, offering coherent responses and following instructions, useful for customer support and virtual assistants. |
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2. **Text Generation**: Generate high-quality, contextually appropriate content such as articles, summaries, explanations, and other forms of written communication based on user prompts. |
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3. **Instruction Following**: Follow specific instructions with accuracy, making it ideal for tasks that require structured guidance, such as technical troubleshooting or educational assistance. |
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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. |
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### Intended Applications: |
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- **Chatbots** for virtual assistance, customer support, or as personal digital assistants. |
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- **Content Creation Tools**, aiding in the generation of written materials, blog posts, or automated responses based on user inputs. |
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- **Educational and Training Systems**, providing explanations and guided learning experiences in various domains. |
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- **Human-AI Interaction** platforms, where the model can follow user instructions to provide personalized assistance or perform specific tasks. |
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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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