ControlLLM
/

Llama-3.1-8B-SynE-FPT

@@ -1,108 +1,111 @@
----
-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
-base_model:
-- meta-llama/Llama-3.1-8B
-model-index:
-- name: Control-LLM-Llama3.1-8B-SynE-Full-Parameter-Tuning
-  results:
-  - task:
-      type: pretraining-evaluation
-    dataset:
-      type: mixed
-      name: Pretraining Evaluation Dataset
-    metrics:
-    - name: exact_match,strict-match (meta_pretrain)
-      type: exact_match
-      value: 0.45445720757159036
-      stderr: 0.0035036029889520047
-      verified: false
-    - name: exact_match,strict-match (meta_bbh_3shot_cot_pretrain)
-      type: exact_match
-      value: 0.6482875134387959
-      stderr: 0.005918167158231359
-      verified: false
-    - name: acc,none (meta_mmlu_5shot_pretrain)
-      type: accuracy
-      value: 0.649480131035465
-      stderr: 0.004026616190778244
-      verified: false
-    - name: exact_match,strict-match (meta_mmlu_pro_5shot_pretrain)
-      type: exact_match
-      value: 0.34956781914893614
-      stderr: 0.004347262544061378
-      verified: false
-  - task:
-      type: chinese-evaluation
-    dataset:
-      type: mixed
-      name: Chinese Evaluation Dataset
-    metrics:
-    - name: acc,none (ceval-valid)
-      type: accuracy
-      value: 0.5898959881129272
-      stderr: 0.012699457390113113
-      verified: false
-    - name: exact_match,strict-match (ceval-valid-pretrain-cot_zh)
-      type: exact_match
-      value: 0.40193164933135217
-      stderr: 0.01265090064840271
-      verified: false
-    - name: acc,none (cmmlu)
-      type: accuracy
-      value: 0.6018822310481782
-      stderr: 0.004420298073040671
-      verified: false
-    - name: exact_match,strict-match (cmmlu_pretrain_cot_zh)
-      type: exact_match
-      value: 0.4425833189431877
-      stderr: 0.004506238417180843
-      verified: false
----
-# Control-LLM-Llama3.1-8B-SynE-Full-Parameter-Tuning
-This is a fine-tuned model of Llama-3.1-8B for muliligual-Chinese tasks on SynE dataset.
-## 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
-### Full Parameter Tuning on Chinese-SynE
-The following plot illustrates the Catastrophic Forgetting of full parameter tuning in terms of hidden states alignment drift.
-![Catastrophic Forgetting](plots/alignment_worst.png)

+---
+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
+base_model:
+- meta-llama/Llama-3.1-8B
+model-index:
+- name: Control-LLM-Llama3.1-8B-SynE-Full-Parameter-Tuning
+  results:
+  - task:
+      type: pretraining-evaluation
+    dataset:
+      type: mixed
+      name: Pretraining Evaluation Dataset
+    metrics:
+    - name: exact_match,strict-match (meta_pretrain)
+      type: exact_match
+      value: 0.45445720757159036
+      stderr: 0.0035036029889520047
+      verified: false
+    - name: exact_match,strict-match (meta_bbh_3shot_cot_pretrain)
+      type: exact_match
+      value: 0.6482875134387959
+      stderr: 0.005918167158231359
+      verified: false
+    - name: acc,none (meta_mmlu_5shot_pretrain)
+      type: accuracy
+      value: 0.649480131035465
+      stderr: 0.004026616190778244
+      verified: false
+    - name: exact_match,strict-match (meta_mmlu_pro_5shot_pretrain)
+      type: exact_match
+      value: 0.34956781914893614
+      stderr: 0.004347262544061378
+      verified: false
+  - task:
+      type: chinese-evaluation
+    dataset:
+      type: mixed
+      name: Chinese Evaluation Dataset
+    metrics:
+    - name: acc,none (ceval-valid)
+      type: accuracy
+      value: 0.5898959881129272
+      stderr: 0.012699457390113113
+      verified: false
+    - name: exact_match,strict-match (ceval-valid-pretrain-cot_zh)
+      type: exact_match
+      value: 0.40193164933135217
+      stderr: 0.01265090064840271
+      verified: false
+    - name: acc,none (cmmlu)
+      type: accuracy
+      value: 0.6018822310481782
+      stderr: 0.004420298073040671
+      verified: false
+    - name: exact_match,strict-match (cmmlu_pretrain_cot_zh)
+      type: exact_match
+      value: 0.4425833189431877
+      stderr: 0.004506238417180843
+      verified: false
+---
+# Control-LLM-Llama3.1-8B-SynE-Full-Parameter-Tuning
+This is a fine-tuned model of Llama-3.1-8B for muliligual-Chinese tasks on SynE 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 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
+### Full Parameter Tuning on Chinese-SynE
+The following plot illustrates the Catastrophic Forgetting of full parameter tuning in terms of hidden states alignment drift.
+![Catastrophic Forgetting](plots/alignment_worst.png)