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--- |
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language: |
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- en |
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license: other |
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library_name: transformers |
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tags: |
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- mergekit |
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- merge |
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- lazymergekit |
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base_model: |
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- Qwen/Qwen2.5-32B-Instruct |
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license_name: tongyi-qianwen |
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license_link: https://huggingface.co/Qwen/Qwen2-72B-Instruct/blob/main/LICENSE |
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pipeline_tag: text-generation |
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model-index: |
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- name: BigQwen2.5-Echo-47B-Instruct |
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results: |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: IFEval (0-Shot) |
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type: HuggingFaceH4/ifeval |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: inst_level_strict_acc and prompt_level_strict_acc |
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value: 73.57 |
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name: strict accuracy |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/BigQwen2.5-Echo-47B-Instruct |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: BBH (3-Shot) |
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type: BBH |
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args: |
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num_few_shot: 3 |
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metrics: |
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- type: acc_norm |
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value: 44.52 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/BigQwen2.5-Echo-47B-Instruct |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MATH Lvl 5 (4-Shot) |
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type: hendrycks/competition_math |
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args: |
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num_few_shot: 4 |
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metrics: |
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- type: exact_match |
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value: 3.47 |
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name: exact match |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/BigQwen2.5-Echo-47B-Instruct |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: GPQA (0-shot) |
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type: Idavidrein/gpqa |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: acc_norm |
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value: 8.61 |
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name: acc_norm |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/BigQwen2.5-Echo-47B-Instruct |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MuSR (0-shot) |
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type: TAUR-Lab/MuSR |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: acc_norm |
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value: 10.19 |
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name: acc_norm |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/BigQwen2.5-Echo-47B-Instruct |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MMLU-PRO (5-shot) |
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type: TIGER-Lab/MMLU-Pro |
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config: main |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 41.49 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/BigQwen2.5-Echo-47B-Instruct |
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name: Open LLM Leaderboard |
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--- |
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# BigQwen2.5-Echo-47B-Instruct |
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 |
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BigQwen2.5-Echo-47B-Instruct is a [Qwen/Qwen2-32B-Instruct](https://huggingface.co/Qwen/Qwen2-72B-Instruct) self-merge made with [MergeKit](https://github.com/arcee-ai/mergekit/tree/main). |
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## 🔉 Echo Merge |
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I've tried a more gradual approach with a **distributed repetition pattern**. Instead of replicating blocks of 8 or more layers, I'm replicating individual layers in these blocks: |
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- First 8 layers: No replication |
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- Next 8 layers: Replicate 2 layers (first one, middle one) |
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- Next 8 layers: Replicate 4 layers (1st, 3rd, 5th, 7th) |
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- Next 8 layers: Replicate 8 layers (all of them) |
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- Next 8 layers: Replicate 4 layers (1st, 3rd, 5th, 7th) |
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- Next 8 layers: Replicate 2 layers (first one, middle one) |
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- First 8 layers: No replication |
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I used this string to visualize it, where 0 are original layers and 1 duplicated ones (the order doesn't matter): |
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``` |
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00000000 1000010000 100100100100 1010101010101010 1010101010101010 100100100100 1000010000 00000000 |
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``` |
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The main idea is that the input/output difference of middle layers is quite small, so replicating a middle layer has a small impact on the output. |
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The additional layers are designed to increase the model's capacity without breaking the information flow, which often creates "insane" self-merges. |
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## 🧩 Configuration |
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The following YAML configuration was used to produce this model: |
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```yaml |
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slices: |
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# First 8 layers: No replication |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [0, 8] |
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# Next 8 layers: Replicate 2 layers |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [8, 9] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [8, 9] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [9, 13] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [13, 14] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [13, 14] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [14, 16] |
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|
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# Next 8 layers: Replicate 4 layers |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [16, 18] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [17, 19] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [18, 20] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [19, 21] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [20, 22] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [21, 23] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [22, 24] |
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|
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# Next 8 layers: Replicate all 8 layers |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [24, 25] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [24, 26] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [25, 27] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [26, 28] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [27, 29] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [28, 30] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [29, 31] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [30, 32] |
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|
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# Middle 8 layers: Replicate all 8 layers |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [32, 33] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [32, 34] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [33, 35] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [34, 36] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [35, 37] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [36, 38] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [37, 39] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [38, 40] |
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|
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# Next 8 layers: Replicate 4 layers |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [40, 42] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [41, 43] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [42, 44] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [43, 45] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [44, 46] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [45, 47] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [46, 48] |
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|
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# Next 8 layers: Replicate 2 layers |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [48, 49] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [48, 49] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [49, 53] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [53, 54] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [53, 54] |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [54, 56] |
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# Last 8 layers: No replication |
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- sources: |
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- model: Qwen/Qwen2.5-32B-Instruct |
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layer_range: [56, 64] |
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merge_method: passthrough |
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dtype: bfloat16 |
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``` |
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## 💻 Usage |
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```python |
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!pip install -qU transformers accelerate |
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from transformers import AutoTokenizer |
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import transformers |
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import torch |
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model = "mlabonne/BigQwen2.5-Echo-47B-Instruct" |
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messages = [{"role": "user", "content": "What is a large language model?"}] |
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tokenizer = AutoTokenizer.from_pretrained(model) |
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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pipeline = transformers.pipeline( |
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"text-generation", |
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model=model, |
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torch_dtype=torch.float16, |
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device_map="auto", |
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) |
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outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) |
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print(outputs[0]["generated_text"]) |
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``` |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_mlabonne__BigQwen2.5-Echo-47B-Instruct) |
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|
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| Metric |Value| |
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|-------------------|----:| |
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|Avg. |30.31| |
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|IFEval (0-Shot) |73.57| |
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|BBH (3-Shot) |44.52| |
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|MATH Lvl 5 (4-Shot)| 3.47| |
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|GPQA (0-shot) | 8.61| |
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|MuSR (0-shot) |10.19| |
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|MMLU-PRO (5-shot) |41.49| |
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