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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 +1,191 @@
+---
+base_model:
+- arcee-ai/Virtuoso-Small-v2
+- sometimesanotion/Qwenvergence-14B-v3-Prose
+- sthenno/tempesthenno-ppo-ckpt40
+- CultriX/Enhanced-TIES-Base-v1
+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 [Linear DELLA](https://arxiv.org/abs/2406.11617) merge method using [CultriX/Enhanced-TIES-Base-v1](https://huggingface.co/CultriX/Enhanced-TIES-Base-v1) as a base.
+
+### Models Merged
+
+The following models were included in the merge:
+* [arcee-ai/Virtuoso-Small-v2](https://huggingface.co/arcee-ai/Virtuoso-Small-v2)
+* [sometimesanotion/Qwenvergence-14B-v3-Prose](https://huggingface.co/sometimesanotion/Qwenvergence-14B-v3-Prose)
+* [sthenno/tempesthenno-ppo-ckpt40](https://huggingface.co/sthenno/tempesthenno-ppo-ckpt40)
+
+### Configuration
+
+The following YAML configuration was used to produce this model:
+
+```yaml
+name: SuperMerge-LayeredTIES-v1
+merge_method: della_linear
+base_model: CultriX/Enhanced-TIES-Base-v1 # Referencing the TIES base model defined below (now inlined)
+tokenizer_source: base
+dtype: float32
+out_dtype: bfloat16
+parameters:
+  int8_mask: true
+  normalize: true
+  rescale: false
+  t: [0.1, 0.3, 0.7, 0.7, 0.4, 0.2]
+
+slices:
+  - sources:
+      - model: CultriX/Enhanced-TIES-Base-v1 # Referencing inlined TIES base
+        layer_range: [0, 8]
+        parameters:
+          weight: 0.7
+      - model: arcee-ai/Virtuoso-Small-v2
+        layer_range: [0, 8]
+        parameters:
+          weight: 0.3
+      - model: sthenno/tempesthenno-ppo-ckpt40
+        layer_range: [0, 8]
+        parameters:
+          weight: 0.0
+      - model: sometimesanotion/Qwenvergence-14B-v3-Prose
+        layer_range: [0, 8]
+        parameters:
+          weight: 0.0
+  - sources:
+      - model: CultriX/Enhanced-TIES-Base-v1 # Referencing inlined TIES base
+        layer_range: [8, 16]
+        parameters:
+          weight: 0.4
+      - model: arcee-ai/Virtuoso-Small-v2
+        layer_range: [8, 16]
+        parameters:
+          weight: 0.3
+      - model: sthenno/tempesthenno-ppo-ckpt40
+        layer_range: [8, 16]
+        parameters:
+          weight: 0.3
+      - model: sometimesanotion/Qwenvergence-14B-v3-Prose
+        layer_range: [8, 16]
+        parameters:
+          weight: 0.0
+  - sources:
+      - model: CultriX/Enhanced-TIES-Base-v1 # Referencing inlined TIES base
+        layer_range: [16, 24]
+        parameters:
+          weight: 0.2
+      - model: arcee-ai/Virtuoso-Small-v2
+        layer_range: [16, 24]
+        parameters:
+          weight: 0.2
+      - model: sthenno/tempesthenno-ppo-ckpt40
+        layer_range: [16, 24]
+        parameters:
+          weight: 0.5
+      - model: sometimesanotion/Qwenvergence-14B-v3-Prose
+        layer_range: [16, 24]
+        parameters:
+          weight: 0.1
+  - sources:
+      - model: CultriX/Enhanced-TIES-Base-v1 # Referencing inlined TIES base
+        layer_range: [24, 32]
+        parameters:
+          weight: 0.25
+      - model: arcee-ai/Virtuoso-Small-v2
+        layer_range: [24, 32]
+        parameters:
+          weight: 0.1
+      - model: sthenno/tempesthenno-ppo-ckpt40
+        layer_range: [24, 32]
+        parameters:
+          weight: 0.4
+      - model: sometimesanotion/Qwenvergence-14B-v3-Prose
+        layer_range: [24, 32]
+        parameters:
+          weight: 0.25
+  - sources:
+      - model: CultriX/Enhanced-TIES-Base-v1 # Referencing inlined TIES base
+        layer_range: [32, 40]
+        parameters:
+          weight: 0.4
+      - model: arcee-ai/Virtuoso-Small-v2
+        layer_range: [32, 40]
+        parameters:
+          weight: 0.0
+      - model: sthenno/tempesthenno-ppo-ckpt40
+        layer_range: [32, 40]
+        parameters:
+          weight: 0.2
+      - model: sometimesanotion/Qwenvergence-14B-v3-Prose
+        layer_range: [32, 40]
+        parameters:
+          weight: 0.4
+  - sources:
+      - model: CultriX/Enhanced-TIES-Base-v1 # Referencing inlined TIES base
+        layer_range: [40, 48]
+        parameters:
+          weight: 0.6
+      - model: arcee-ai/Virtuoso-Small-v2
+        layer_range: [40, 48]
+        parameters:
+          weight: 0.0
+      - model: sthenno/tempesthenno-ppo-ckpt40
+        layer_range: [40, 48]
+        parameters:
+          weight: 0.1
+      - model: sometimesanotion/Qwenvergence-14B-v3-Prose
+        layer_range: [40, 48]
+        parameters:
+          weight: 0.3
+
+
+
+# Commentary:
+# =============================================================================
+# SuperMerge-LayeredTIES-v1 Commentary:
+#
+# This configuration combines the strengths of both Enhanced-LayeredSlerp-v1 and SuperMerge-Enhanced-v1.
+# It leverages the robust foundation of a TIES-merged base model (Enhanced-TIES-Base-v1) and applies
+# the layer-wise module approach and fine-grained weight control from SuperMerge-Enhanced-v1 in a SLERP merge.
+#
+# Key Features:
+#   - TIES-Merged Base Foundation:  Uses 'Enhanced-TIES-Base-v1' as the base model for the SLERP merge.
+#     This TIES base provides a selectively merged and potentially more efficient starting point, incorporating
+#     strengths from multiple models (Virtuoso, Phi-4, Qwenvergence, DeepSeek) with density control.
+#
+#   - Layer-wise Module Integration in SLERP:  Maintains the module-based slice structure from SuperMerge-Enhanced-v1.
+#     The SLERP merge now combines the TIES-merged base with specialized modules for Reasoning, IFEval, and MATH/Knowledge
+#     at different layer ranges, using explicit weights for fine-grained control.
+#
+#   - Benchmark-Driven Iterative Weight Tuning:  The configuration is designed to be optimized through a
+#     benchmark-driven iterative weight tuning process (as described in the refined SuperMerge-Enhanced-v1 approach).
+#     The initial weights provided are starting points and need to be systematically tuned based on benchmark results.
+#
+# Tuning Process (Same as Refined SuperMerge-Enhanced-v1):
+#   1. Initial Benchmarking: Run a full benchmark suite.
+#   2. Performance Analysis: Examine per-benchmark scores and compare to source models.
+#   3. Targeted Weight Adjustments: Adjust layer weights based on performance analysis (e.g., increase IFEval module weight
+#      in early layers if IFEval is weak).
+#   4. Iterate: Repeat steps 1-3. Make small, incremental adjustments in each iteration.
+#
+# Rationale:
+#   - By using a TIES-merged base, we aim to create a more robust and potentially efficient foundation for the SLERP merge.
+#   - The layer-wise module approach and fine-grained weights in SLERP still allow for precise control over the blending
+#     of specialized capabilities at different network depths, building upon the solid TIES base.
+#   - The emphasis on a benchmark-driven iterative weight tuning process remains crucial for achieving optimal performance.
+#
+# Next Steps:
+#   - Implement this configuration using MergeKit.
+#   - Run initial benchmarks to establish a baseline.
+#   - Begin the iterative benchmark-driven weight tuning process to optimize performance.
+# =============================================================================
+```

added_tokens.json

@@ -0,0 +1,24 @@
+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

config.json

@@ -0,0 +1,29 @@
+{
+  "_name_or_path": "CultriX/Enhanced-TIES-Base-v1",
+  "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": "4.48.2",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151665
+}

mergekit_config.yml

@@ -0,0 +1,157 @@
+name: SuperMerge-LayeredTIES-v1
+merge_method: della_linear
+base_model: CultriX/Enhanced-TIES-Base-v1 # Referencing the TIES base model defined below (now inlined)
+tokenizer_source: base
+dtype: float32
+out_dtype: bfloat16
+parameters:
+  int8_mask: true
+  normalize: true
+  rescale: false
+  t: [0.1, 0.3, 0.7, 0.7, 0.4, 0.2]
+
+slices:
+  - sources:
+      - model: CultriX/Enhanced-TIES-Base-v1 # Referencing inlined TIES base
+        layer_range: [0, 8]
+        parameters:
+          weight: 0.7
+      - model: arcee-ai/Virtuoso-Small-v2
+        layer_range: [0, 8]
+        parameters:
+          weight: 0.3
+      - model: sthenno/tempesthenno-ppo-ckpt40
+        layer_range: [0, 8]
+        parameters:
+          weight: 0.0
+      - model: sometimesanotion/Qwenvergence-14B-v3-Prose
+        layer_range: [0, 8]
+        parameters:
+          weight: 0.0
+  - sources:
+      - model: CultriX/Enhanced-TIES-Base-v1 # Referencing inlined TIES base
+        layer_range: [8, 16]
+        parameters:
+          weight: 0.4
+      - model: arcee-ai/Virtuoso-Small-v2
+        layer_range: [8, 16]
+        parameters:
+          weight: 0.3
+      - model: sthenno/tempesthenno-ppo-ckpt40
+        layer_range: [8, 16]
+        parameters:
+          weight: 0.3
+      - model: sometimesanotion/Qwenvergence-14B-v3-Prose
+        layer_range: [8, 16]
+        parameters:
+          weight: 0.0
+  - sources:
+      - model: CultriX/Enhanced-TIES-Base-v1 # Referencing inlined TIES base
+        layer_range: [16, 24]
+        parameters:
+          weight: 0.2
+      - model: arcee-ai/Virtuoso-Small-v2
+        layer_range: [16, 24]
+        parameters:
+          weight: 0.2
+      - model: sthenno/tempesthenno-ppo-ckpt40
+        layer_range: [16, 24]
+        parameters:
+          weight: 0.5
+      - model: sometimesanotion/Qwenvergence-14B-v3-Prose
+        layer_range: [16, 24]
+        parameters:
+          weight: 0.1
+  - sources:
+      - model: CultriX/Enhanced-TIES-Base-v1 # Referencing inlined TIES base
+        layer_range: [24, 32]
+        parameters:
+          weight: 0.25
+      - model: arcee-ai/Virtuoso-Small-v2
+        layer_range: [24, 32]
+        parameters:
+          weight: 0.1
+      - model: sthenno/tempesthenno-ppo-ckpt40
+        layer_range: [24, 32]
+        parameters:
+          weight: 0.4
+      - model: sometimesanotion/Qwenvergence-14B-v3-Prose
+        layer_range: [24, 32]
+        parameters:
+          weight: 0.25
+  - sources:
+      - model: CultriX/Enhanced-TIES-Base-v1 # Referencing inlined TIES base
+        layer_range: [32, 40]
+        parameters:
+          weight: 0.4
+      - model: arcee-ai/Virtuoso-Small-v2
+        layer_range: [32, 40]
+        parameters:
+          weight: 0.0
+      - model: sthenno/tempesthenno-ppo-ckpt40
+        layer_range: [32, 40]
+        parameters:
+          weight: 0.2
+      - model: sometimesanotion/Qwenvergence-14B-v3-Prose
+        layer_range: [32, 40]
+        parameters:
+          weight: 0.4
+  - sources:
+      - model: CultriX/Enhanced-TIES-Base-v1 # Referencing inlined TIES base
+        layer_range: [40, 48]
+        parameters:
+          weight: 0.6
+      - model: arcee-ai/Virtuoso-Small-v2
+        layer_range: [40, 48]
+        parameters:
+          weight: 0.0
+      - model: sthenno/tempesthenno-ppo-ckpt40
+        layer_range: [40, 48]
+        parameters:
+          weight: 0.1
+      - model: sometimesanotion/Qwenvergence-14B-v3-Prose
+        layer_range: [40, 48]
+        parameters:
+          weight: 0.3
+
+
+
+# Commentary:
+# =============================================================================
+# SuperMerge-LayeredTIES-v1 Commentary:
+#
+# This configuration combines the strengths of both Enhanced-LayeredSlerp-v1 and SuperMerge-Enhanced-v1.
+# It leverages the robust foundation of a TIES-merged base model (Enhanced-TIES-Base-v1) and applies
+# the layer-wise module approach and fine-grained weight control from SuperMerge-Enhanced-v1 in a SLERP merge.
+#
+# Key Features:
+#   - TIES-Merged Base Foundation:  Uses 'Enhanced-TIES-Base-v1' as the base model for the SLERP merge.
+#     This TIES base provides a selectively merged and potentially more efficient starting point, incorporating
+#     strengths from multiple models (Virtuoso, Phi-4, Qwenvergence, DeepSeek) with density control.
+#
+#   - Layer-wise Module Integration in SLERP:  Maintains the module-based slice structure from SuperMerge-Enhanced-v1.
+#     The SLERP merge now combines the TIES-merged base with specialized modules for Reasoning, IFEval, and MATH/Knowledge
+#     at different layer ranges, using explicit weights for fine-grained control.
+#
+#   - Benchmark-Driven Iterative Weight Tuning:  The configuration is designed to be optimized through a
+#     benchmark-driven iterative weight tuning process (as described in the refined SuperMerge-Enhanced-v1 approach).
+#     The initial weights provided are starting points and need to be systematically tuned based on benchmark results.
+#
+# Tuning Process (Same as Refined SuperMerge-Enhanced-v1):
+#   1. Initial Benchmarking: Run a full benchmark suite.
+#   2. Performance Analysis: Examine per-benchmark scores and compare to source models.
+#   3. Targeted Weight Adjustments: Adjust layer weights based on performance analysis (e.g., increase IFEval module weight
+#      in early layers if IFEval is weak).
+#   4. Iterate: Repeat steps 1-3. Make small, incremental adjustments in each iteration.
+#
+# Rationale:
+#   - By using a TIES-merged base, we aim to create a more robust and potentially efficient foundation for the SLERP merge.
+#   - The layer-wise module approach and fine-grained weights in SLERP still allow for precise control over the blending
+#     of specialized capabilities at different network depths, building upon the solid TIES base.
+#   - The emphasis on a benchmark-driven iterative weight tuning process remains crucial for achieving optimal performance.
+#
+# Next Steps:
+#   - Implement this configuration using MergeKit.
+#   - Run initial benchmarks to establish a baseline.
+#   - Begin the iterative benchmark-driven weight tuning process to optimize performance.
+# =============================================================================

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special_tokens_map.json

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tokenizer.json

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+size 11421896

tokenizer_config.json

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+}