--- license: llama3.1 datasets: - nvidia/OpenMathInstruct-2 language: - en base_model: - meta-llama/Llama-3.1-8B-Instruct model-index: - name: Control-LLM-Llama3.1-8B-Math16 results: - task: type: code-evaluation dataset: type: mixed name: Code Evaluation Dataset metrics: - name: pass_at_1,n=1 (code_instruct) type: pass_at_1 value: 0.7840083073727934 stderr: 0.013257237506304915 verified: false - name: pass_at_1,n=1 (humaneval_greedy_instruct) type: pass_at_1 value: 0.8170731707317073 stderr: 0.03028135999593353 verified: false - name: pass_at_1,n=1 (humaneval_plus_greedy_instruct) type: pass_at_1 value: 0.7439024390243902 stderr: 0.03418746588364997 verified: false - name: pass_at_1,n=1 (mbpp_plus_0shot_instruct) type: pass_at_1 value: 0.8042328042328042 stderr: 0.0204357309715418 verified: false - name: pass_at_1,n=1 (mbpp_sanitized_0shot_instruct) type: pass_at_1 value: 0.7587548638132295 stderr: 0.02673991635681605 verified: false - task: type: original-capability dataset: type: meta/Llama-3.1-8B-Instruct-evals name: Llama-3.1-8B-Instruct-evals Dataset dataset_path: "meta-llama/llama-3.1-8_b-instruct-evals" dataset_name: "Llama-3.1-8B-Instruct-evals__arc_challenge__details" metrics: - name: exact_match,strict-match (original_capability_instruct) type: exact_match value: 0.5630801459168563 stderr: 0.0028483348465514185 verified: false - name: exact_match,strict-match (meta_arc_0shot_instruct) type: exact_match value: 0.8248927038626609 stderr: 0.01113972223585952 verified: false - name: exact_match,strict-match (meta_gpqa_0shot_cot_instruct) type: exact_match value: 0.296875 stderr: 0.021609729061250887 verified: false - name: exact_match,strict-match (meta_mmlu_0shot_instruct) type: exact_match value: 0.6815980629539952 stderr: 0.003931452244804845 verified: false - name: exact_match,strict-match (meta_mmlu_pro_5shot_instruct) type: exact_match value: 0.4093251329787234 stderr: 0.004482884901882547 verified: false library_name: transformers pipeline_tag: text-generation --- # Control-LLM-Llama3.1-8B-Math16 This is a fine-tuned model of Llama-3.1-8B-Instruct for mathematical tasks on OpenCoder SFT dataset. ## Linked Paper This model is associated with the paper: [Control-LLM](https://arxiv.org/abs/2501.10979). ## Evaluation Results Here is an overview of the evaluation results and findings: ### Benchmark Result and Catastrophic Forgetting on OpenCoder The following plot illustrates benchmark result and catastrophic forgetting mitigation on the OpenCoder SFT dataset. ![Catastrophic Forgetting](plots/catastrophic_forgetting_opencoder.png) ### Benchmark Results Table The table below summarizes evaluation results across coding tasks and original capabilities. | **Model** | **MB+** | **MS** | **HE+** | **HE** | **C-Avg** | **ARC** | **GP** | **MLU** | **MLUP** | **O-Avg** | **Overall** | |--------------------|---------|---------|---------|---------|-----------|---------|---------|---------|----------|-----------|-------------| | Llama3.1-8B-Ins | 70.4 | 67.7 | 66.5 | 70.7 | 69.1 | 83.4 | 29.9 | 72.4 | 46.7 | 60.5 | 64.8 | | OpenCoder-8B-Ins | 81.2 | 76.3 | 78.0 | 82.3 | 79.5 | 8.2 | 25.4 | 37.4 | 11.3 | 24.6 | 52.1 | | **Full Param Tune**| 75.1 | 69.6 | 71.3 | 76.8 | 73.3 | 24.4 | 21.9 | 43.0 | 19.2 | 31.5 | 52.4 | | Partial Param Tune | 75.7 | 71.6 | 74.4 | 79.3 | 75.0 | 70.2 | 28.1 | 60.7 | 32.4 | 48.3 | 61.7 | | Stack Expansion | 77.2 | 72.8 | 73.2 | 78.7 | 75.6 | 80.0 | 26.3 | 66.6 | 38.2 | 54.2 | 64.9 | | Hybrid Expansion* | 77.5 | 73.5 | **76.2**| **82.3**| 77.1 | 80.9 | **32.6**| 68.1 | 40.3 | 56.0 | 66.6 | | **Control LLM*** | **80.4**| **75.9**| 74.4 | 81.1 | **78.3** | **82.5**| 29.7 | **68.2**| **40.9** | **56.3** | **67.3** | --- ### Explanation: - **MB+**: MBPP Plus - **MS**: MBPP Sanitized - **HE+**: HumanEval Plus - **HE**: HumanEval - **C-Avg**: Coding - Size Weighted Average across MB+, MS, HE+, and HE - **ARC**: ARC benchmark - **GP**: GPQA benchmark - **MLU**: MMLU (Massive Multitask Language Understanding) - **MLUP**: MMLU Pro - **O-Avg**: Original Capability - Size Weighted Average across ARC, GPQA, MMLU, and MMLU Pro - **Overall**: Combined average across all tasks