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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,120 @@
+---
+base_model:
+- CultriX/SeQwence-14B-EvolMerge
+- hotmailuser/QwenSlerp2-14B
+- qingy2024/Fusion4-14B-Instruct
+- djuna/Q2.5-Veltha-14B-0.5
+- CultriX/Qwen2.5-14B-Emerged
+- allknowingroger/QwenSlerp6-14B
+- CultriX/Qwen2.5-14B-Wernickev3
+- sometimesanotion/Lamarck-14B-v0.6
+library_name: transformers
+tags:
+- mergekit
+- merge
+
+---
+# merge
+
+This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
+
+## Merge Details
+### Merge Method
+
+This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [CultriX/Qwen2.5-14B-Wernickev3](https://huggingface.co/CultriX/Qwen2.5-14B-Wernickev3) as a base.
+
+### Models Merged
+
+The following models were included in the merge:
+* [CultriX/SeQwence-14B-EvolMerge](https://huggingface.co/CultriX/SeQwence-14B-EvolMerge)
+* [hotmailuser/QwenSlerp2-14B](https://huggingface.co/hotmailuser/QwenSlerp2-14B)
+* [qingy2024/Fusion4-14B-Instruct](https://huggingface.co/qingy2024/Fusion4-14B-Instruct)
+* [djuna/Q2.5-Veltha-14B-0.5](https://huggingface.co/djuna/Q2.5-Veltha-14B-0.5)
+* [CultriX/Qwen2.5-14B-Emerged](https://huggingface.co/CultriX/Qwen2.5-14B-Emerged)
+* [allknowingroger/QwenSlerp6-14B](https://huggingface.co/allknowingroger/QwenSlerp6-14B)
+* [sometimesanotion/Lamarck-14B-v0.6](https://huggingface.co/sometimesanotion/Lamarck-14B-v0.6)
+
+### Configuration
+
+The following YAML configuration was used to produce this model:
+
+```yaml
+merge_method: dare_ties  # Specifies the merge method as dare_ties, known for its high-performance potential.
+base_model: CultriX/Qwen2.5-14B-Wernickev3  # Sets the base model, a strong multitask performer, for parameter alignment.
+dtype: bfloat16  # Defines the data type for model weights as bfloat16, for efficient memory and computation.
+out_dtype: bfloat16  # Sets the output data type to bfloat16 for consistency with the input.
+
+parameters:
+  epsilon: 0.008  # Fine-tunes parameter scaling, improving the quality of the merge and stability.
+  lambda: 1.8  # Prioritizes high-impact parameters, useful for reasoning and multitask performance.
+  normalize: true  # Ensures parameter normalization, preventing any instability during the merge.
+  rescale: true  # Adjusts parameter scales across different models, improving compatibility.
+  int8_mask: false  # Disables int8 masking, preserving full precision for better parameter alignment.
+
+adaptive_merge_parameters:
+  task_weights:  # Defines task weights, to emphasize different areas of model performance
+    tinyArc: 1.6  # Sets a moderate priority for logical reasoning tasks.
+    tinyHellaswag: 1.5  # Sets a medium priority for contextual understanding tasks.
+    tinyMMLU: 1.8  # Gives a higher priority to multi-domain knowledge benchmarks.
+    tinyTruthfulQA: 1.9  # Gives a higher priority to factual accuracy and QA tasks.
+    tinyTruthfulQA_mc1: 1.75  # Slightly reduced priority, but still important, for multiple-choice reasoning.
+    tinyWinogrande: 1.75  # Sets a medium priority for more complex contextual reasoning tasks.
+    IFEval: 2.30  # Sets a high priority for instruction-following evaluation, as it is often a weak point for models.
+    BBH: 2.05  # Gives a high priority to the big bench hard task, critical for complex reasoning.
+    MATH: 2.70  # Sets the highest priority for mathematical reasoning tasks.
+    GPQA: 2.20  # Gives a balanced priority to graduate-level question-answering tasks.
+    MUSR: 2.15  # Gives a slightly lower, but still high, priority to multi-step reasoning tasks.
+    MMLU-PRO: 2.00 # Gives a high priority to domain-specific multitask benchmark performance.
+  smoothing_factor: 0.03  # Sets the smoothing factor, for a better blending of different task performance.
+
+gradient_clipping:  # Defines gradient clipping values for each model, for training stability.
+  CultriX/Qwen2.5-14B-Wernickev3: 0.89  # Sets the clipping value for the base model, a core component of the merge, and gives it a higher stability.
+  djuna/Q2.5-Veltha-14B-0.5: 0.92  # Sets the clipping value for the djuna model, a strong performer in reasoning tasks.
+  CultriX/SeQwence-14B-EvolMerge: 0.87  # Sets the clipping value for this model, which is a balanced multi-task performer.
+  qingy2024/Fusion4-14B-Instruct: 0.93  # Sets the clipping value for this model, emphasizing stability for mathematical reasoning.
+  CultriX/Qwen2.5-14B-Emerged: 0.88  # Sets the clipping value for this model, which provides multi-task support.
+  sometimesanotion/Lamarck-14B-v0.6: 0.89  # Sets the clipping value for this model, to enhance the multi-step reasoning capabilities.
+  allknowingroger/QwenSlerp6-14B: 0.90  # Sets the clipping value for this model, which supports nuanced reasoning tasks.
+  hotmailuser/QwenSlerp2-14B: 0.91  # Sets the clipping value for this model, with slightly increased stability for logical reasoning tasks.
+
+models:  # Defines all the models that are going to be included in the merge.
+  - model: CultriX/Qwen2.5-14B-Wernickev3 # Defines the base model, the main backbone of the merge, that offers good multi-task capabilities.
+    parameters: # Defines the weight and density that will be used for the model.
+      weight: 0.32  # Sets the weight of the model to 0.32, which is the dominant contribution to the final model.
+      density: 0.78  # Sets a high density of 0.78, to preserve its parameters, as this is a key component of the merge.
+
+  - model: djuna/Q2.5-Veltha-14B-0.5  # Defines the djuna model, a strong performer in factual and reasoning tasks.
+    parameters:  # Defines the weight and density that will be used for the model.
+      weight: 0.28  # Sets the weight of the model to 0.28, to prioritize reasoning performance.
+      density: 0.77  # Sets a balanced density of 0.77, to enhance its reasoning abilities.
+
+  - model: allknowingroger/QwenSlerp6-14B  # Defines the allknowingroger model, which has good performance and reasoning capabilities.
+    parameters:  # Defines the weight and density that will be used for the model.
+      weight: 0.15  # Sets the weight of the model to 0.15, which has a moderate contribution to the final model.
+      density: 0.72  # Sets a density of 0.72, for an effective parameter integration into the final model.
+
+  - model: CultriX/SeQwence-14B-EvolMerge # Defines the CultriX/SeQwence model, which is a good multi-task contributor.
+    parameters:  # Defines the weight and density that will be used for the model.
+      weight: 0.12  # Sets the weight of the model to 0.12, a lower weight for its contribution.
+      density: 0.62  # Sets a density of 0.62, for balanced performance.
+
+  - model: qingy2024/Fusion4-14B-Instruct  # Defines the qingy model, which excels at mathematical reasoning.
+    parameters:  # Defines the weight and density that will be used for the model.
+      weight: 0.09  # Sets the weight of the model to 0.09, for a specific focus on mathematical reasoning tasks.
+      density: 0.75  # Sets a density of 0.75, for preserving its strengths in mathematical tasks.
+
+  - model: CultriX/Qwen2.5-14B-Emerged  # Defines the CultriX/Qwen2.5-14B-Emerged model, a good multi-task performer.
+    parameters:  # Defines the weight and density that will be used for the model.
+      weight: 0.08 # Sets the weight of the model to 0.08, for a smaller, but still useful, contribution.
+      density: 0.69 # Sets a density of 0.69, to balance its contributions.
+
+  - model: sometimesanotion/Lamarck-14B-v0.6 # Defines the sometimesanotion/Lamarck model, which is useful for multi-step reasoning.
+    parameters:  # Defines the weight and density that will be used for the model.
+      weight: 0.06 # Sets the weight of the model to 0.06, for a lower performing task model.
+      density: 0.62 # Sets the density to 0.62 for multi-step reasoning tasks.
+
+  - model: hotmailuser/QwenSlerp2-14B  # Defines the hotmailuser model, with strong performance in reasoning and multi-task performance.
+    parameters:  # Defines the weight and density that will be used for the model.
+      weight: 0.12  # Sets the weight of the model to 0.12, for its balanced contributions.
+      density: 0.66  # Sets the density of the model to 0.66, for better parameter integration.
+```

added_tokens.json

@@ -0,0 +1,24 @@
+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
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+  "<|fim_pad|>": 151662,
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+  "<|object_ref_end|>": 151647,
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+  "<|vision_start|>": 151652
+}

config.json

@@ -0,0 +1,29 @@
+{
+  "_name_or_path": "CultriX/Qwen2.5-14B-Wernickev3",
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 13824,
+  "max_position_embeddings": 131072,
+  "max_window_layers": 48,
+  "model_type": "qwen2",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 48,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.46.2",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151665
+}

mergekit_config.yml

@@ -0,0 +1,78 @@
+merge_method: dare_ties  # Specifies the merge method as dare_ties, known for its high-performance potential.
+base_model: CultriX/Qwen2.5-14B-Wernickev3  # Sets the base model, a strong multitask performer, for parameter alignment.
+dtype: bfloat16  # Defines the data type for model weights as bfloat16, for efficient memory and computation.
+out_dtype: bfloat16  # Sets the output data type to bfloat16 for consistency with the input.
+
+parameters:
+  epsilon: 0.008  # Fine-tunes parameter scaling, improving the quality of the merge and stability.
+  lambda: 1.8  # Prioritizes high-impact parameters, useful for reasoning and multitask performance.
+  normalize: true  # Ensures parameter normalization, preventing any instability during the merge.
+  rescale: true  # Adjusts parameter scales across different models, improving compatibility.
+  int8_mask: false  # Disables int8 masking, preserving full precision for better parameter alignment.
+
+adaptive_merge_parameters:
+  task_weights:  # Defines task weights, to emphasize different areas of model performance
+    tinyArc: 1.6  # Sets a moderate priority for logical reasoning tasks.
+    tinyHellaswag: 1.5  # Sets a medium priority for contextual understanding tasks.
+    tinyMMLU: 1.8  # Gives a higher priority to multi-domain knowledge benchmarks.
+    tinyTruthfulQA: 1.9  # Gives a higher priority to factual accuracy and QA tasks.
+    tinyTruthfulQA_mc1: 1.75  # Slightly reduced priority, but still important, for multiple-choice reasoning.
+    tinyWinogrande: 1.75  # Sets a medium priority for more complex contextual reasoning tasks.
+    IFEval: 2.30  # Sets a high priority for instruction-following evaluation, as it is often a weak point for models.
+    BBH: 2.05  # Gives a high priority to the big bench hard task, critical for complex reasoning.
+    MATH: 2.70  # Sets the highest priority for mathematical reasoning tasks.
+    GPQA: 2.20  # Gives a balanced priority to graduate-level question-answering tasks.
+    MUSR: 2.15  # Gives a slightly lower, but still high, priority to multi-step reasoning tasks.
+    MMLU-PRO: 2.00 # Gives a high priority to domain-specific multitask benchmark performance.
+  smoothing_factor: 0.03  # Sets the smoothing factor, for a better blending of different task performance.
+
+gradient_clipping:  # Defines gradient clipping values for each model, for training stability.
+  CultriX/Qwen2.5-14B-Wernickev3: 0.89  # Sets the clipping value for the base model, a core component of the merge, and gives it a higher stability.
+  djuna/Q2.5-Veltha-14B-0.5: 0.92  # Sets the clipping value for the djuna model, a strong performer in reasoning tasks.
+  CultriX/SeQwence-14B-EvolMerge: 0.87  # Sets the clipping value for this model, which is a balanced multi-task performer.
+  qingy2024/Fusion4-14B-Instruct: 0.93  # Sets the clipping value for this model, emphasizing stability for mathematical reasoning.
+  CultriX/Qwen2.5-14B-Emerged: 0.88  # Sets the clipping value for this model, which provides multi-task support.
+  sometimesanotion/Lamarck-14B-v0.6: 0.89  # Sets the clipping value for this model, to enhance the multi-step reasoning capabilities.
+  allknowingroger/QwenSlerp6-14B: 0.90  # Sets the clipping value for this model, which supports nuanced reasoning tasks.
+  hotmailuser/QwenSlerp2-14B: 0.91  # Sets the clipping value for this model, with slightly increased stability for logical reasoning tasks.
+
+models:  # Defines all the models that are going to be included in the merge.
+  - model: CultriX/Qwen2.5-14B-Wernickev3 # Defines the base model, the main backbone of the merge, that offers good multi-task capabilities.
+    parameters: # Defines the weight and density that will be used for the model.
+      weight: 0.32  # Sets the weight of the model to 0.32, which is the dominant contribution to the final model.
+      density: 0.78  # Sets a high density of 0.78, to preserve its parameters, as this is a key component of the merge.
+
+  - model: djuna/Q2.5-Veltha-14B-0.5  # Defines the djuna model, a strong performer in factual and reasoning tasks.
+    parameters:  # Defines the weight and density that will be used for the model.
+      weight: 0.28  # Sets the weight of the model to 0.28, to prioritize reasoning performance.
+      density: 0.77  # Sets a balanced density of 0.77, to enhance its reasoning abilities.
+
+  - model: allknowingroger/QwenSlerp6-14B  # Defines the allknowingroger model, which has good performance and reasoning capabilities.
+    parameters:  # Defines the weight and density that will be used for the model.
+      weight: 0.15  # Sets the weight of the model to 0.15, which has a moderate contribution to the final model.
+      density: 0.72  # Sets a density of 0.72, for an effective parameter integration into the final model.
+
+  - model: CultriX/SeQwence-14B-EvolMerge # Defines the CultriX/SeQwence model, which is a good multi-task contributor.
+    parameters:  # Defines the weight and density that will be used for the model.
+      weight: 0.12  # Sets the weight of the model to 0.12, a lower weight for its contribution.
+      density: 0.62  # Sets a density of 0.62, for balanced performance.
+
+  - model: qingy2024/Fusion4-14B-Instruct  # Defines the qingy model, which excels at mathematical reasoning.
+    parameters:  # Defines the weight and density that will be used for the model.
+      weight: 0.09  # Sets the weight of the model to 0.09, for a specific focus on mathematical reasoning tasks.
+      density: 0.75  # Sets a density of 0.75, for preserving its strengths in mathematical tasks.
+
+  - model: CultriX/Qwen2.5-14B-Emerged  # Defines the CultriX/Qwen2.5-14B-Emerged model, a good multi-task performer.
+    parameters:  # Defines the weight and density that will be used for the model.
+      weight: 0.08 # Sets the weight of the model to 0.08, for a smaller, but still useful, contribution.
+      density: 0.69 # Sets a density of 0.69, to balance its contributions.
+
+  - model: sometimesanotion/Lamarck-14B-v0.6 # Defines the sometimesanotion/Lamarck model, which is useful for multi-step reasoning.
+    parameters:  # Defines the weight and density that will be used for the model.
+      weight: 0.06 # Sets the weight of the model to 0.06, for a lower performing task model.
+      density: 0.62 # Sets the density to 0.62 for multi-step reasoning tasks.
+
+  - model: hotmailuser/QwenSlerp2-14B  # Defines the hotmailuser model, with strong performance in reasoning and multi-task performance.
+    parameters:  # Defines the weight and density that will be used for the model.
+      weight: 0.12  # Sets the weight of the model to 0.12, for its balanced contributions.
+      density: 0.66  # Sets the density of the model to 0.66, for better parameter integration.

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special_tokens_map.json

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+{
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+    "<|im_end|>",
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+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
+size 11421896

tokenizer_config.json

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+}