Expert68/llama2_13b_instructed_version2
Expert68/llama2_13b_instructed_version2 is a 13 billion parameter instruction-tuned language model based on the Llama 2 architecture, featuring a 4096-token context length. It is fine-tuned on a diverse collection of datasets including Stanford Alpaca, Open Assistant, LIMA, CodeAlpaca, GPT-4 Generated Data, and UltraChat. This model is designed for general-purpose instruction following, with a particular emphasis on conversational AI and code-related tasks due to its training data.
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Expert68/llama2_13b_instructed_version2 Overview
This model is a 13 billion parameter instruction-tuned variant of the Llama 2 architecture, designed for robust performance across various natural language processing tasks. It leverages a 4096-token context window, enabling it to process and generate longer, more coherent responses.
Key Capabilities
- Instruction Following: Fine-tuned on a broad range of instruction datasets, enhancing its ability to understand and execute user commands.
- Multilingual Support: Incorporates the Open Assistant dataset, which includes multilingual data, contributing to its ability to handle non-English inputs.
- Code Generation & Understanding: Training on CodeAlpaca 20k specifically equips the model with capabilities for programming-related tasks.
- Conversational AI: Datasets like Open Assistant and UltraChat contribute to its proficiency in engaging in dialogue and generating human-like conversations.
Training Data Highlights
The model's training regimen includes a diverse set of high-quality instruction-following datasets:
- Stanford Alpaca (en): Enhances general instruction-following abilities.
- Open Assistant (multilingual): Provides diverse conversational and instruction data across multiple languages.
- LIMA (en): Focuses on high-quality, concise responses.
- CodeAlpaca 20k (en): Specifically targets code generation and comprehension.
- GPT-4 Generated Data (en&zh): Incorporates advanced instruction patterns and reasoning from a powerful source model.
- UltraChat (en): Further refines conversational and multi-turn dialogue capabilities.
Good For
- Developing chatbots and conversational agents.
- Assisting with code generation and debugging.
- General-purpose text generation and instruction-based tasks.
- Applications requiring a balance of reasoning and creative output.
Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.