--- language: - en license: apache-2.0 library_name: transformers tags: - mergekit - merge - lazymergekit base_model: - Qwen/Qwen2.5-32B-Instruct license_name: tongyi-qianwen license_link: https://huggingface.co./Qwen/Qwen2-72B-Instruct/blob/main/LICENSE pipeline_tag: text-generation model-index: - name: BigQwen2.5-Echo-47B-Instruct results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 73.57 name: strict accuracy source: url: >- https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/BigQwen2.5-Echo-47B-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 44.52 name: normalized accuracy source: url: >- https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/BigQwen2.5-Echo-47B-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 3.47 name: exact match source: url: >- https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/BigQwen2.5-Echo-47B-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 8.61 name: acc_norm source: url: >- https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/BigQwen2.5-Echo-47B-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 10.19 name: acc_norm source: url: >- https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/BigQwen2.5-Echo-47B-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 41.49 name: accuracy source: url: >- https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=mlabonne/BigQwen2.5-Echo-47B-Instruct name: Open LLM Leaderboard --- # BigQwen2.5-Echo-47B-Instruct ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/98GiKtmH1AtHHbIbOUH4Y.jpeg) 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). ## 🔉 Echo Merge 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: - First 8 layers: No replication - Next 8 layers: Replicate 2 layers (first one, middle one) - Next 8 layers: Replicate 4 layers (1st, 3rd, 5th, 7th) - Next 8 layers: Replicate 8 layers (all of them) - Next 8 layers: Replicate 4 layers (1st, 3rd, 5th, 7th) - Next 8 layers: Replicate 2 layers (first one, middle one) - First 8 layers: No replication I used this string to visualize it, where 0 are original layers and 1 duplicated ones (the order doesn't matter): ``` 00000000 1000010000 100100100100 1010101010101010 1010101010101010 100100100100 1000010000 00000000 ``` 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. The additional layers are designed to increase the model's capacity without breaking the information flow, which often creates "insane" self-merges. ## 🏆 Evaluation | Metric |**BigQwen2.5-Echo-47B-Instruct**|BigQwen2.5-52B-Instruct|Qwen2.5-32B-Instruct| |-------------------|----:|----:|----:| |Avg. |30.31|37.42|36.17| |IFEval (0-Shot) |73.57|79.29|83.46| |BBH (3-Shot) |44.52|59.81|56.49| |MATH Lvl 5 (4-Shot)| 3.47|17.82|0| |GPQA (0-shot) | 8.61| 6.94|11.74| |MuSR (0-shot) |10.19|10.45|13.5| |MMLU-PRO (5-shot) |41.49|50.22|51.85| ## 🧩 Configuration The following YAML configuration was used to produce this model: ```yaml slices: # First 8 layers: No replication - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [0, 8] # Next 8 layers: Replicate 2 layers - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [8, 9] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [8, 9] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [9, 13] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [13, 14] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [13, 14] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [14, 16] # Next 8 layers: Replicate 4 layers - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [16, 18] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [17, 19] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [18, 20] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [19, 21] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [20, 22] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [21, 23] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [22, 24] # Next 8 layers: Replicate all 8 layers - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [24, 25] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [24, 26] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [25, 27] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [26, 28] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [27, 29] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [28, 30] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [29, 31] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [30, 32] # Middle 8 layers: Replicate all 8 layers - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [32, 33] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [32, 34] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [33, 35] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [34, 36] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [35, 37] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [36, 38] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [37, 39] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [38, 40] # Next 8 layers: Replicate 4 layers - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [40, 42] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [41, 43] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [42, 44] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [43, 45] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [44, 46] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [45, 47] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [46, 48] # Next 8 layers: Replicate 2 layers - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [48, 49] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [48, 49] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [49, 53] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [53, 54] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [53, 54] - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [54, 56] # Last 8 layers: No replication - sources: - model: Qwen/Qwen2.5-32B-Instruct layer_range: [56, 64] merge_method: passthrough dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/BigQwen2.5-Echo-47B-Instruct" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```