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--- |
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license: creativeml-openrail-m |
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datasets: |
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- HuggingFaceTB/smoltalk |
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language: |
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- en |
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base_model: |
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- meta-llama/Llama-3.2-1B-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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- SmolTalk |
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- ollama |
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- bin |
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--- |
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## Llama-SmolTalk-3.2-1B-Instruct Model File |
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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. |
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| File Name [ Updated Files ] | 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 README | Uploaded | |
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| `config.json` | 1.03 kB | Configuration file | Uploaded | |
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| `generation_config.json` | 248 Bytes | Configuration for text generation | Uploaded | |
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| `pytorch_model.bin` | 2.47 GB | PyTorch model weights | Uploaded (LFS) | |
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| `special_tokens_map.json` | 477 Bytes | Special token mappings | Uploaded | |
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| `tokenizer.json` | 17.2 MB | Tokenizer configuration | Uploaded (LFS) | |
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| `tokenizer_config.json` | 57.4 kB | Additional tokenizer settings | Uploaded | |
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| Model Type | Size | Context Length | Link | |
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|------------|------|----------------|------| |
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| GGUF | 1B | - | [🤗 Llama-SmolTalk-3.2-1B-Instruct-GGUF](https://huggingface.co/prithivMLmods/Llama-SmolTalk-3.2-1B-Instruct-GGUF) | |
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### Key Features: |
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1. **Instruction-Tuned Performance**: Optimized to understand and execute user-provided instructions across diverse domains. |
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2. **Lightweight Architecture**: With just 1 billion parameters, the model provides efficient computation and storage without compromising output quality. |
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3. **Versatile Use Cases**: Suitable for tasks like content generation, conversational interfaces, and basic problem-solving. |
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### Intended Applications: |
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- **Conversational AI**: Engage users with dynamic and contextually aware dialogue. |
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- **Content Generation**: Produce summaries, explanations, or other creative text outputs efficiently. |
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- **Instruction Execution**: Follow user commands to generate precise and relevant responses. |
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### Technical Details: |
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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. |
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**Model Type**: GGUF |
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**Size**: 1B Parameters |
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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. |
