Upload README.md with huggingface_hub

README.md

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+
+---
+
+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
+
+---
+
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+
+
+# 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.