Gemma2-9B-AdvancedFuse
Gemma2-9B-AdvancedFuse is an experimental, open-source large language model (LLM) with 9 billion parameters. It aims to combine the strengths of FuseAI/FuseChat-Gemma-2-9B-Instruct and jsgreenawalt/gemma-2-9B-it-advanced-v2.1 through additive linear merging, further fine-tuned on a 12K row dataset from agentlans/crash-course for enhanced chat and instruct performance, including math and multilingual prompts.
Capabilities
- Text Generation: Generates coherent emails, summaries, and notes. This model card was primarily generated by the model itself.
- Instruction Following: Demonstrates strong ability to understand and execute instructions in conversational settings.
- Roleplaying: Can engage in third-person narrative roleplay but may exhibit common GPT expressions or clichés.
Limitations
As with most large language models:
- Factual Errors: May generate incorrect or outdated information due to data biases.
- Mathematical Operations: Struggles with mathematical calculations requiring symbolic reasoning despite its finetuning data.
- Handling Unsafe Input: May generate unsafe, biased, or malicious content if provided inappropriate input. Careful prompt engineering is recommended.
Model Usage Guidelines
- Use clear and specific instructions for optimal performance.
- Verify generated outputs for factual accuracy when critical information is involved.
- Avoid providing inputs that could lead to harmful or unethical responses.
- Consider using human review, especially in high-stakes applications.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here! Summarized results can be found here!
Metric | Value (%) |
---|---|
Average | 20.02 |
IFEval (0-Shot) | 15.43 |
BBH (3-Shot) | 40.52 |
MATH Lvl 5 (4-Shot) | 7.55 |
GPQA (0-shot) | 11.30 |
MuSR (0-shot) | 11.99 |
MMLU-PRO (5-shot) | 33.34 |
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Model tree for agentlans/Gemma2-9B-AdvancedFuse
Base model
FuseAI/FuseChat-Gemma-2-9B-InstructDataset used to train agentlans/Gemma2-9B-AdvancedFuse
Evaluation results
- averaged accuracy on IFEval (0-Shot)Open LLM Leaderboard15.430
- normalized accuracy on BBH (3-Shot)test set Open LLM Leaderboard40.520
- exact match on MATH Lvl 5 (4-Shot)test set Open LLM Leaderboard7.550
- acc_norm on GPQA (0-shot)Open LLM Leaderboard11.300
- acc_norm on MuSR (0-shot)Open LLM Leaderboard11.990
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard33.340