QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF
This is quantized version of 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 |
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.