macadeliccc/gemma-orchid-7b-dpo is an 8.5 billion parameter Gemma-based language model developed by macadeliccc, fine-tuned using DPO. This model is designed with strong communicational skills and function calling capabilities. It was trained on approximately 80,000 samples, making it suitable for applications requiring both natural language interaction and tool use.
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Model Overview
macadeliccc/gemma-orchid-7b-dpo is an 8.5 billion parameter Gemma-based language model, fine-tuned using Direct Preference Optimization (DPO). This model represents the second checkpoint in an ongoing project, focusing on enhancing both communicational skills and function calling abilities.
Key Capabilities
- Function Calling: The model is capable of understanding and generating function calls, making it suitable for integration with external tools and APIs.
- Strong Communicational Skills: Designed to handle a wide variety of conversational and text generation tasks effectively.
- DPO Fine-tuning: Trained on approximately 80,000 samples using datasets like Thermostatic/flowers, Intel/orca_dpo_pairs, jondurbin/truthy-dpo-v0.1, and glaiveai/glaive_function_calling_v2, which includes a blend of open-source model generations and function calling data.
Performance
Evaluations on the Open LLM Leaderboard show an average score of 64.37, with specific metrics including:
- AI2 Reasoning Challenge (25-Shot): 62.88
- HellaSwag (10-Shot): 80.95
- MMLU (5-Shot): 61.41
- TruthfulQA (0-shot): 53.27
- Winogrande (5-shot): 77.51
- GSM8k (5-shot): 50.19
Good For
- Applications requiring function calling: Ideal for scenarios where the model needs to interact with external systems or execute specific actions.
- General conversational AI: Its strong communicational base makes it suitable for chatbots, virtual assistants, and content generation.
- Further fine-tuning: The base model, macadeliccc/gemma-function-calling-7b, is explicitly mentioned as suitable for further fine-tuning with custom datasets.