QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF
This is quantized version of prithivMLmods/Llama-SmolTalk-3.2-1B-Instruct created using llama.cpp
Original Model Card
Updated Files for Model Uploads 🤗
| File Name [ Updated Files ] | Size | Description | Upload Status |
|---|---|---|---|
.gitattributes | 1.57 kB | Git attributes configuration file | Uploaded |
README.md | 42 Bytes | Initial README | Uploaded |
config.json | 1.03 kB | Configuration file | Uploaded |
generation_config.json | 248 Bytes | Configuration for text generation | Uploaded |
pytorch_model.bin | 2.47 GB | PyTorch model weights | Uploaded (LFS) |
special_tokens_map.json | 477 Bytes | Special token mappings | Uploaded |
tokenizer.json | 17.2 MB | Tokenizer configuration | Uploaded (LFS) |
tokenizer_config.json | 57.4 kB | Additional tokenizer settings | Uploaded |
| Model Type | Size | Context Length | Link |
|---|---|---|---|
| GGUF | 1B | - | 🤗 Llama-SmolTalk-3.2-1B-Instruct-GGUF |
The Llama-SmolTalk-3.2-1B-Instruct model is a lightweight, instruction-tuned model designed for efficient text generation and conversational AI tasks. With a 1B parameter architecture, this model strikes a balance between performance and resource efficiency, making it ideal for applications requiring concise, contextually relevant outputs. The model has been fine-tuned to deliver robust instruction-following capabilities, catering to both structured and open-ended queries.
Key Features:
- Instruction-Tuned Performance: Optimized to understand and execute user-provided instructions across diverse domains.
- Lightweight Architecture: With just 1 billion parameters, the model provides efficient computation and storage without compromising output quality.
- Versatile Use Cases: Suitable for tasks like content generation, conversational interfaces, and basic problem-solving.
Intended Applications:
- Conversational AI: Engage users with dynamic and contextually aware dialogue.
- Content Generation: Produce summaries, explanations, or other creative text outputs efficiently.
- Instruction Execution: Follow user commands to generate precise and relevant responses.
Technical Details:
The model leverages PyTorch for training and inference, with a tokenizer optimized for seamless text input processing. It comes with essential configuration files, including config.json, generation_config.json, and tokenization files (tokenizer.json and special_tokens_map.json). The primary weights are stored in a PyTorch binary format (pytorch_model.bin), ensuring easy integration with existing workflows.
Model Type: GGUF
Size: 1B Parameters
The Llama-SmolTalk-3.2-1B-Instruct model is an excellent choice for lightweight text generation tasks, offering a blend of efficiency and effectiveness for a wide range of applications.