OmniCorso-7B Overview
OmniCorso-7B is a 7 billion parameter language model developed by macadeliccc. It was created through a Slerp merge of two base models: macadeliccc/MBX-7B-v3-DPO and mlabonne/OmniBeagle-7B. This merging strategy aims to combine the strengths of its constituent models.
Key Capabilities & Performance
- General Reasoning: Achieves an average score of 61.73% across a custom evaluation suite including AGIEval (45.89%), GPT4All (77.66%), TruthfulQA (74.12%), and Bigbench (49.24%).
- Open LLM Leaderboard: Demonstrates competitive performance with an average score of 75.74% on the Hugging Face Open LLM Leaderboard, including:
- AI2 Reasoning Challenge (25-Shot): 72.70%
- HellaSwag (10-Shot): 88.70%
- MMLU (5-Shot): 64.91%
- TruthfulQA (0-shot): 73.43%
- Winogrande (5-shot): 83.74%
- GSM8k (5-shot): 70.96%
- Context Length: Supports a context length of 4096 tokens.
- Quantization Support: Available in various quantization formats, including GGUF and Exllamav2 (e.g., 6.5-bit recommended for good size/performance trade-off).
When to Use OmniCorso-7B
OmniCorso-7B is suitable for applications requiring a capable 7B parameter model with strong general reasoning and conversational abilities. Its balanced performance across diverse benchmarks makes it a versatile choice for tasks such as:
- General-purpose chatbots and assistants
- Question answering and information retrieval
- Code generation and understanding (as suggested by the example, though not explicitly benchmarked for code)
- Reasoning-intensive tasks where its AGIEval and Bigbench scores are relevant.