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README.md

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+---
+library_name: transformers
+tags:
+- '#mergekit '
+- '#arcee-ai'
+datasets:
+- arcee-ai/sec-data-mini
+---
+## Quick Summary
+
+This model is an adaptation of the `mistralai/Mistral-7B-Instruct-v0.2`, refined through the application of layer pruning techniques as detailed in the paper "The Unreasonable Ineffectiveness of the Deeper Layers." It incorporates methodologies from the `MergeKit` and `PruneMe` repositories to optimize its structure, focusing on reducing redundancy within the model's deeper layers without compromising its ability to generate coherent text. The model is maintained by Arcee-ai and represents a practical implementation of computational efficiency improvements in Large Language Models (LLMs), aiming to balance performance with resource usage effectively.
+
+<img src="https://cdn-uploads.huggingface.co/production/uploads/654aa1d86167ff03f70e32f9/CwiPyc9GIft4Iy_Howe9h.webp" width="300" height="auto">
+
+### Model Description
+
+This model represents a specialized iteration of the `mistralai/Mistral-7B-Instruct-v0.2`, optimized for efficiency and performance through selective layer pruning. Developed by Arcee-ai, it leverages insights from the "The Unreasonable Ineffectiveness of the Deeper Layers" research. The pruning process was informed by the `MergeKit` and `PruneMe` tools, focusing on eliminating redundant layers to ensure a leaner, more efficient model capable of generating high-quality text outputs.
+
+### Model Sources
+
+- **Pruning:** [PruneMe GitHub (unofficial)](https://github.com/arcee-ai/PruneMe)
+- **Paper:** ["The Unreasonable Ineffectiveness of the Deeper Layers"](https://arxiv.org/pdf/2403.17887.pdf)
+- **Merging Repository:** [MergeKit GitHub](https://github.com/arcee-ai/mergekit)
+
+## Uses
+
+This pruned model is designed for a range of NLP tasks, with a focus on maintaining or even enhancing the model's original capabilities in generating coherent text, despite the reduction in its size. It stands as a testament to the feasibility of layer pruning in preserving the essential functional attributes of a model while offering a template for computational resource optimization.
+
+### Downstream Use
+
+The pruned model serves as a robust foundation for fine-tuning on specific tasks and is an ideal candidate for exploring continuous pre-training opportunities. Its development is a direct application of principles outlined in "The Unreasonable Ineffectiveness of the Deeper Layers," utilizing the `MergeKit` and `PruneMe` repositories for practical pruning implementation. This model is a step forward in efficient model design, demonstrating the potential for significant reductions in computational resource requirements without detrimental effects on performance.

config.json

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+{
+    "_name_or_path": "./merged",
+    "architectures": [
+        "MistralForCausalLM"
+    ],
+    "attention_dropout": 0.0,
+    "bos_token_id": 1,
+    "eos_token_id": 2,
+    "hidden_act": "silu",
+    "hidden_size": 4096,
+    "initializer_range": 0.02,
+    "intermediate_size": 14336,
+    "max_position_embeddings": 32768,
+    "model_type": "mistral",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 24,
+    "num_key_value_heads": 8,
+    "rms_norm_eps": 1e-05,
+    "rope_theta": 1000000.0,
+    "sliding_window": null,
+    "tie_word_embeddings": false,
+    "torch_dtype": "float32",
+    "transformers_version": "4.39.3",
+    "use_cache": true,
+    "vocab_size": 32000,
+    "quantization_config": {
+        "quant_method": "exl2",
+        "version": "0.0.16",
+        "bits": 4.8,
+        "head_bits": 6,
+        "calibration": {
+            "rows": 100,
+            "length": 2048,
+            "dataset": "(default)"
+        }
+    }
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.39.3"
+}

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