ControlLLM/Llama-3.1-8B-OpenCoder16-Instruct

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.

Linked Open Source code - training, eval and benchmark

This model is associated with the github: Control-LLM.

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.

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

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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