---
license: llama3.1
datasets:
- survivi/Llama-3-SynE-Dataset
- hfl/stem_zh_instruction
- llamafactory/alpaca_zh
- llamafactory/alpaca_gpt4_zh
- hfl/ruozhiba_gpt4
- codingsteven/Llama-3-8B-chat
language:
- zh
metrics:
- accuracy
base_model:
- meta-llama/Llama-3.1-8B
model-index:
- name: Control-LLM-Llama3.1-8B-SynE-Hybrid
  results:
  - task:
      type: pretraining-evaluation
    dataset:
      type: mixed
      name: Pretraining Evaluation Dataset
    metrics:
    - name: exact_match,strict-match (meta_pretrain)
      type: exact_match
      value: 0.4677775980154236
      stderr: 0.0035271375539740195
      verified: false
    - name: exact_match,strict-match (meta_bbh_3shot_cot_pretrain)
      type: exact_match
      value: 0.6516664106896022
      stderr: 0.005904999312183116
      verified: false
    - name: acc,none (meta_mmlu_5shot_pretrain)
      type: accuracy
      value: 0.6574562028201111
      stderr: 0.004004907112115045
      verified: false
    - name: exact_match,strict-match (meta_mmlu_pro_5shot_pretrain)
      type: exact_match
      value: 0.36826795212765956
      stderr: 0.004397416024070344
      verified: false
  - task:
      type: chinese-evaluation
    dataset:
      type: mixed
      name: Chinese Evaluation Dataset
    metrics:
    - name: exact_match,strict-match (zh_pretrain_multishot)
      type: exact_match
      value: 0.4448483910891089
      stderr: 0.004279257037413458
      verified: false
    - name: acc,none (ceval-valid)
      type: accuracy
      value: 0.5891530460624071
      stderr: 0.012995719777231915
      verified: false
    - name: exact_match,strict-match (ceval-valid-pretrain-cot_zh)
      type: exact_match
      value: 0.44650817236255574
      stderr: 0.013132438471522461
      verified: false
    - name: acc,none (cmmlu)
      type: accuracy
      value: 0.578742876877914
      stderr: 0.004459355253649275
      verified: false
    - name: exact_match,strict-match (cmmlu_pretrain_cot_zh)
      type: exact_match
      value: 0.4446554999136591
      stderr: 0.004526020080338497
      verified: false
pipeline_tag: text-generation
library_name: transformers
---

# Control-LLM-Llama3.1-8B-SynE-Hybrid
This is a fine-tuned model of Llama-3.1-8B for muliligual-Chinese tasks on SynE dataset by Control LLM-Hybrid.

## Linked Paper
This model is associated with the paper: [Control LLM: Controlled Evolution for Intelligence Retention in LLM](https://huggingface.co/papers/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 Results Table

The table below summarizes evaluation results across Chinese tasks and original capabilities.

| **Model**          | **CEval** | **CEvalC** | **CMMLU** | **CMMLUC** | **C-Avg** | **BBH** | **MLU** | **MLUP** | **O-Avg** | **Overall** |
|--------------------|-----------|------------|-----------|------------|-----------|---------|---------|----------|-----------|-------------|
| Llama3.1-8B        | 48.3      | 12.8       | 51.1      | 14.1       | 13.9      | 65.2    | 65.4    | 35.5     | 45.9      | 29.9        |
| Llama-3-SynE       | 57.7      | 22.3       | 57.1      | 22.8       | 22.8      | 61.9    | 64.0    | 32.6     | 42.9      | 32.9        |
| Full Param Tune   | 59.0      | 40.2       | **60.2**  | 44.3       | 43.8      | 64.8    | 64.9    | 35.0     | 45.4      | 44.6        |
| Stack Expansion    | 56.0      | 32.7       | 55.2      | 33.4       | 33.3      | 62.3    | 65.6    | 35.3     | 44.8      | 39.1        |
| Concat-Lerp*       | 57.1      | 34.8       | 57.0      | 37.4       | 37.1      | 64.4    | 64.6    | 35.8     | 45.9      | 41.5        |
| **Hybrid Expansion**| **58.9** | 44.7       | 57.9      | 44.3       | 44.4      | 65.1    | **65.7**| 36.9     | 46.8      | 45.6        |
| **Control LLM***   | 57.0      | **44.7**   | 56.0      | **44.9**   | **44.8**  | **68.2**| 65.6    | **37.9** | **48.5**  | **46.7**    |

---

### Explanation:
- **CEval**: Chinese Evaluation
- **CEvalC**: Chinese Evaluation (CoT - Chain of Thought)
- **CMMLU**: Chinese MMLU
- **CMMLUC**: Chinese MMLU (CoT)
- **C-Avg**: Chinese - Size Weighted Average across CEval, CEvalC, CMMLU, and CMMLUC
- **BBH**: BigBench Hard
- **MLU**: MMLU (Massive Multitask Language Understanding)
- **MLUP**: MMLU Pro
- **O-Avg**: Original Capability - Size Weighted Average across BBH, MLU, and MLUP
- **Overall**: Combined average across all tasks