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Add pipeline tag, link to paper

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This PR adds a link to the paper [Control LLM: Controlled Evolution for Intelligence Retention in LLM](https://huggingface.co/papers/2501.10979).
It also adds the relevant `pipeline_tag` and `library_name`.

Files changed (1) hide show
  1. README.md +7 -3
README.md CHANGED
@@ -9,8 +9,12 @@ datasets:
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  - codingsteven/Llama-3-8B-chat
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  language:
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  - zh
 
 
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  base_model:
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  - meta-llama/Llama-3.1-8B
 
 
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  model-index:
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  - name: Control-LLM-Llama3.1-8B-SynE-Concat16-Dlerp
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  results:
@@ -74,10 +78,10 @@ model-index:
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  ---
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  # Control-LLM-Llama3.1-8B-SynE-Concat16-Dlerp
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- This is a fine-tuned model of Llama-3.1-8B for muliligual-Chinese tasks on SynE dataset by Control LLM-Concat16-Dlerp.
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  ## Linked Paper
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- This model is associated with the paper: [Control-LLM](https://arxiv.org/abs/2501.10979).
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  ## Evaluation Results
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  Here is an overview of the evaluation results and findings:
@@ -108,4 +112,4 @@ The table below summarizes evaluation results across Chinese tasks and original
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  - **MLU**: MMLU (Massive Multitask Language Understanding)
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  - **MLUP**: MMLU Pro
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  - **O-Avg**: Original Capability - Size Weighted Average across BBH, MLU, and MLUP
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- - **Overall**: Combined average across all tasks
 
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  - codingsteven/Llama-3-8B-chat
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  language:
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  - zh
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+ metrics:
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+ - accuracy
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  base_model:
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  - meta-llama/Llama-3.1-8B
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+ pipeline_tag: text-generation
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+ library_name: transformers
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  model-index:
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  - name: Control-LLM-Llama3.1-8B-SynE-Concat16-Dlerp
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  results:
 
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  ---
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  # Control-LLM-Llama3.1-8B-SynE-Concat16-Dlerp
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+ This is a fine-tuned model of Llama-3.1-8B for muliligual-Chinese tasks on SynE dataset by Control LLM-Concat16-Dlerp, as described in [Control LLM: Controlled Evolution for Intelligence Retention in LLM](https://huggingface.co/papers/2501.10979).
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  ## Linked Paper
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+ This model is associated with the paper: [Control-LLM](https://arxiv.org/abs/2410.14745).
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  ## Evaluation Results
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  Here is an overview of the evaluation results and findings:
 
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  - **MLU**: MMLU (Massive Multitask Language Understanding)
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  - **MLUP**: MMLU Pro
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  - **O-Avg**: Original Capability - Size Weighted Average across BBH, MLU, and MLUP
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+ - **Overall**: Combined average across all tasks