---

license: creativeml-openrail-m
datasets:
- HuggingFaceTB/smoltalk
language:
- en
base_model:
- meta-llama/Llama-3.2-1B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- Llama
- Llama-CPP
- SmolTalk
- ollama
- bin

---

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# QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF
This is quantized version of [prithivMLmods/Llama-SmolTalk-3.2-1B-Instruct](https://huggingface.co./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](https://huggingface.co./prithivMLmods/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:
1. **Instruction-Tuned Performance**: Optimized to understand and execute user-provided instructions across diverse domains.
2. **Lightweight Architecture**: With just 1 billion parameters, the model provides efficient computation and storage without compromising output quality.
3. **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.