README.md

---
license: llama3.1
datasets:
- nvidia/OpenMathInstruct-2
language:
- en
metrics:
- accuracy
base_model:
- meta-llama/Llama-3.1-8B-Instruct
model-index:
- name: Control-LLM-Llama3.1-8B-Math16
  results:
  - task:
      type: math-evaluation
    dataset:
      type: parquet
      name: Math, Math Hard, GSM8K
      dataset_kwargs:
        data_files: "https://github.com/linkedin/ControlLLM/blob/main/src/controlllm/inference/llm_eval_harness/additional_tasks/math/joined_math.parquet"
    metrics:
    - name: exact_match,none
      type: exact_match
      value: 0.6327358367133324
      stderr: 0.0052245703347459605
      verified: false
    - name: exact_match,none (gsm8k_0shot_instruct)
      type: exact_match
      value: 0.9052312357846853
      stderr: 0.008067791560015407
      verified: false
    - name: exact_match,none (meta_math_0shot_instruct)
      type: exact_match
      value: 0.6276
      stderr: 0.006837616441401548
      verified: false
    - name: exact_match,none (meta_math_hard_0shot_instruct)
      type: exact_match
      value: 0.3806646525679758
      stderr: 0.013349170720370741
      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
      type: exact_match
      value: 0.5723263625528227
      stderr: 0.002858377993520894
      verified: false
    - name: exact_match,strict-match (meta_arc_0shot_instruct)
      type: exact_match
      value: 0.7974248927038626
      stderr: 0.01178043813618557
      verified: false
    - name: exact_match,strict-match (meta_gpqa_0shot_cot_instruct)
      type: exact_match
      value: 0.25223214285714285
      stderr: 0.02054139101648797
      verified: false
    - name: exact_match,strict-match (meta_mmlu_0shot_instruct)
      type: exact_match
      value: 0.6837345107534539
      stderr: 0.0039243761987253515
      verified: false
    - name: exact_match,strict-match (meta_mmlu_pro_5shot_instruct)
      type: exact_match
      value: 0.4324301861702128
      stderr: 0.004516653585262379
      verified: false
pipeline_tag: text-generation
library_name: transformers
---

# Control-LLM-Llama3.1-8B-Math16
This is a fine-tuned model of Llama-3.1-8B-Instruct for mathematical tasks on OpenMath2 dataset, as described in the paper [Control LLM: Controlled Evolution for Intelligence Retention in LLM](https://huggingface.co/papers/2501.10979).

## Linked Paper
This model is associated with the paper: [Control-LLM](https://arxiv.org/abs/2501.10979).

## Linked Open Source code - training, eval and benchmark
This model is associated with the github: [Control-LLM](https://github.com/linkedin/ControlLLM).

## Evaluation Results
Here is an overview of the evaluation results and findings:

### Benchmark Result and Catastrophic Forgetting on OpenMath
The following plot illustrates benchmark result and catastrophic forgetting mitigation on the OpenMath2 dataset.

![Catastrophic Forgetting](plots/catastrophic_forgetting_openmath.png)

### Alignment Comparison
The plot below highlights the alignment comparison of the model trained with Control LLM and Full Parameter Tuning.

![Alignment Comparison](plots/alignment_comparison.png)

### Benchmark Results Table
The table below summarizes evaluation results across mathematical tasks and original capabilities.

| **Model**         | **MH** | **M**  | **G8K** | **M-Avg** | **ARC** | **GPQA** | **MLU** | **MLUP** | **O-Avg** | **Overall** |
|-------------------|--------|--------|---------|-----------|---------|----------|---------|----------|-----------|-------------|
| Llama3.1-8B-Inst  | 23.7   | 50.9   | 85.6    | 52.1      | 83.4    | 29.9     | 72.4    | 46.7     | 60.5      | 56.3        |
| OpenMath2-Llama3  | 38.4   | 64.1   | 90.3    | 64.3      | 45.8    | 1.3      | 4.5     | 19.5     | 12.9      | 38.6        |
| **Full Tune**      | **38.5**| **63.7**| 90.2    | **63.9**  | 58.2    | 1.1      | 7.3     | 23.5     | 16.5      | 40.1        |
| Partial Tune      | 36.4   | 61.4   | 89.0    | 61.8      | 66.2    | 6.0      | 25.7    | 30.9     | 29.3      | 45.6        |
| Stack Exp.        | 35.6   | 61.0   | 90.8    | 61.8      | 69.3    | 18.8     | 61.8    | 43.1     | 53.3      | 57.6        |
| Hybrid Exp.       | 34.4   | 61.1   | 90.1    | 61.5      | **81.8**| **25.9** | 67.2    | **43.9** | 57.1      | 59.3        |
| **Control LLM***   | 38.1   | 62.7   | **90.4**| 63.2      | 79.7    | 25.2     | **68.1**| 43.6     | **57.2**  | **60.2**    |

---
### Explanation:
- **MH**: MathHard
- **M**: Math
- **G8K**: GSM8K
- **M-Avg**: Math - Average across MathHard, Math, and GSM8K
- **ARC**: ARC benchmark
- **GPQA**: General knowledge QA
- **MLU**: MMLU (Massive Multitask Language Understanding)
- **MLUP**: MMLU Pro
- **O-Avg**: Orginal Capability - Average across ARC, GPQA, MMLU, and MMLUP
- **Overall**: Combined average across all tasks