myra/counterexamples_llama_chat
myra/counterexamples_llama_chat is a 7 billion parameter language model fine-tuned from Meta's Llama-2-7b-chat-hf. This model is designed for chat-based applications, leveraging the Llama 2 architecture for conversational tasks. Its specific fine-tuning objective and dataset are not publicly detailed, suggesting a specialized but undisclosed application focus. The model operates with a context length of 4096 tokens.
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Model Overview
myra/counterexamples_llama_chat is a 7 billion parameter language model, fine-tuned from the meta-llama/Llama-2-7b-chat-hf base model. While the specific dataset used for fine-tuning is not disclosed, it is optimized for chat-based interactions, inheriting the conversational capabilities of the Llama 2 architecture.
Key Characteristics
- Base Model: Fine-tuned from Meta's Llama-2-7b-chat-hf.
- Parameter Count: 7 billion parameters.
- Context Length: Supports a context window of 4096 tokens.
- Training Details: Trained with a learning rate of 2e-05, using Adam optimizer, and a cosine learning rate scheduler over 3 epochs. The training utilized a multi-GPU setup with 4 devices.
Intended Use Cases
Given its base model and fine-tuning, this model is generally suitable for:
- Developing conversational AI agents.
- Generating human-like text in response to prompts.
- Applications requiring general-purpose chat capabilities.
Further details on specific intended uses and limitations are not provided in the available documentation.