--- datasets: - agentlans/crash-course base_model: - google/gemma-2-9b-it - FuseAI/FuseChat-Gemma-2-9B-Instruct - jsgreenawalt/gemma-2-9B-it-advanced-v2.1 tags: - gemma2 language: - en pipeline_tag: text-generation license: gemma model-index: - name: Gemma2-9B-AdvancedFuse results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: wis-k/instruction-following-eval split: train args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 15.43 name: averaged accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=agentlans%2FGemma2-9B-AdvancedFuse name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: SaylorTwift/bbh split: test args: num_few_shot: 3 metrics: - type: acc_norm value: 40.52 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=agentlans%2FGemma2-9B-AdvancedFuse name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: lighteval/MATH-Hard split: test args: num_few_shot: 4 metrics: - type: exact_match value: 7.55 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=agentlans%2FGemma2-9B-AdvancedFuse name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa split: train args: num_few_shot: 0 metrics: - type: acc_norm value: 11.3 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=agentlans%2FGemma2-9B-AdvancedFuse name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 11.99 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=agentlans%2FGemma2-9B-AdvancedFuse name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 33.34 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=agentlans%2FGemma2-9B-AdvancedFuse name: Open LLM Leaderboard --- # 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](https://huggingface.co/fuseai/fusechat-gemma-2-9b-instruct) and [jsgreenawalt/gemma-2-9B-it-advanced-v2.1](https://huggingface.co/jsgreenawalt/gemma-2-9b-it-advanced-v2.1) through additive linear merging, further fine-tuned on a 12K row dataset from [agentlans/crash-course](https://huggingface.co/datasets/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 1. Use clear and specific instructions for optimal performance. 2. Verify generated outputs for factual accuracy when critical information is involved. 3. Avoid providing inputs that could lead to harmful or unethical responses. 4. Consider using human review, especially in high-stakes applications. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/agentlans__Gemma2-9B-AdvancedFuse-details)! Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=agentlans%2FGemma2-9B-AdvancedFuse&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)! | 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|