ddobokki/Llama-2-70b-orca-200k
The ddobokki/Llama-2-70b-orca-200k model is a 69 billion parameter language model based on the Llama-2 architecture. It has been fine-tuned using a 200k sample of the OpenOrca dataset, specializing in instruction-following and conversational tasks. This model is designed for general-purpose text generation and understanding, particularly excelling in response quality due to its Orca-based training.
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Model Overview
The ddobokki/Llama-2-70b-orca-200k is a 69 billion parameter model built upon the Llama-2 architecture. This model distinguishes itself through its fine-tuning process, which utilized a 200k sample from the OpenOrca dataset. This targeted training aims to enhance its instruction-following capabilities and conversational fluency.
Key Characteristics
- Architecture: Llama-2 base model.
- Parameter Count: 69 billion parameters.
- Training Data: Fine-tuned with a 200k sample from the OpenOrca dataset, focusing on high-quality instruction-tuning data.
- Context Length: Supports a context length of 32768 tokens.
Intended Use Cases
This model is particularly well-suited for applications requiring robust instruction-following and nuanced conversational interactions. Its training on the OpenOrca dataset suggests strong performance in:
- General-purpose text generation.
- Question answering and dialogue systems.
- Tasks benefiting from detailed and accurate responses based on given instructions.
Prompt Format
The model expects a specific prompt template for optimal performance:
### Human: {Human}
### Assistant: {Assistant}This format helps the model understand the distinction between user input and its expected output, leading to more coherent and relevant responses.