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llama-3.2-1b-instruct

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-# LlaMa 3.2 1B Instruct
-## Quantized Model Files
-LlaMa 3.2 1B is available in several formats, catering to different computational needs:
-- **ggml-model-q8_0.gguf**: 8-bit quantization, providing robust performance.
-- **ggml-model-f16.gguf**: Standard 16-bit floating-point format, for enhanced precision.
-These formats, ranging from 4-bit to 16-bit, accommodate various computational environments, from resource-constrained devices to high-end server
-## Model Information
-The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks.
-**Model Developer:** Meta
-**Model Architecture:** Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
-|  | Training Data | Params | Input modalities | Output modalities | Context Length | GQA | Shared Embeddings | Token count | Knowledge cutoff |
-| :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- |
-| Llama 3.2 (text only)  | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code  | 128k | Yes | Yes | Up to 9T tokens | December 2023 |
-|  |  | 3B (3.21B) | Multilingual Text | Multilingual Text and code  |  |  |  |  |  |
-**Supported Languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly.
-**Llama 3.2 Model Family:** Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.
-**Model Release Date:** Sept 25, 2024
-**Status:** This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety.
-**License:** Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement).
-**Feedback:** Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama-models/tree/main/models/llama3_2). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
-## **Ethical Considerations and Limitations**
-LlaMa 3.2 1B is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2, developers should perform safety testing and tuning tailored to their specific applications of the model.
-Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide/)

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+# LLaMA 3.2 1B Instruct
+## 1. **Model Title**
+- **Name**: LLaMA 3.2 1B Instruct
+- **Parameter Size**: 1B (1.23B)
+## 2. **Quantization Information**
+- **Available Formats**:
+  - **ggml-model-q8_0.gguf**: 8-bit quantization for resource efficiency and good performance.
+  - **ggml-model-f16.gguf**: Half-precision (16-bit) floating-point format for enhanced precision.
+- **Quantization Library**: llama.cpp
+- **Use Cases**: Recommended for tasks such as multilingual dialogue, text generation, and summarization.
+## 3. **Model Brief**
+LLaMA 3.2 1B Instruct is a multilingual instruction-tuned language model, optimized for various dialogue tasks. It has been trained on a diverse set of publicly available data and performs well on common NLP benchmarks. The model architecture leverages improved transformer optimizations, making it effective for both text-only and code tasks.
+- **Purpose**: Multilingual dialogue generation and summarization.
+- **Model Family**: LLaMA 3.2
+- **Architecture**: Auto-regressive Transformer with Grouped-Query Attention (GQA)
+- **Training Data**: A mix of publicly available multilingual data, covering up to 9T tokens.
+- **Supported Languages**: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
+- **Release Date**: September 25, 2024
+- **Context Length**: 128k tokens
+- **Knowledge Cutoff**: December 2023
+## 4. **Core Library Information**
+- **Library**: llama.cpp
+  - *[Repository Link](https://github.com/ggerganov/llama.cpp)*
+- **Model Base**: [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct)
+## 5. **Safety and Responsible Use**
+LLaMA 3.2 1B is designed with safety in mind but still carries inherent risks due to its generative nature. It may produce biased, harmful, or unpredictable responses, especially for less-tested languages or sensitive prompts.
+- **Testing and Risk Assessment**: Initial testing has focused on English outputs, and coverage for other languages is ongoing.
+- **Limitations**: As with most LLMs, LLaMA 3.2 may not fully adhere to user instructions or safety guidelines and might exhibit unexpected behavior.
+- **Responsible Use Guidelines**: For deployment, thorough testing is advised to align outputs with application-specific safety requirements. Refer to the [Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide/) for more details.