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orig/README.md

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+---
+base_model:
+- SanjiWatsuki/Kunoichi-DPO-v2-7B
+- Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context
+library_name: transformers
+tags:
+- mergekit
+- merge
+- alpaca
+- mistral
+---
+Thanks to @Epiculous for the dope model/ help with llm backends and support overall.
+
+Id like to also thank @kalomaze for the dope sampler additions to ST. 
+
+@SanjiWatsuki Thank you very much for the help, and the model!
+
+ST users can find the TextGenPreset in the folder labeled so.
+
+![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642265bc01c62c1e4102dc36/9obNSalcJqCilQwr_4ssM.jpeg)
+
+Quants:
+# mergedmodel
+
+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 SLERP merge method.
+
+### Models Merged
+
+The following models were included in the merge:
+* [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B)
+* [Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context](https://huggingface.co/Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context)
+
+### Configuration
+
+The following YAML configuration was used to produce this model:
+
+```yaml
+slices:
+  - sources:
+      - model: SanjiWatsuki/Kunoichi-DPO-v2-7B
+        layer_range: [0, 32]
+      - model: Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context
+        layer_range: [0, 32]
+merge_method: slerp
+base_model: SanjiWatsuki/Kunoichi-DPO-v2-7B
+parameters:
+  t:
+    - filter: self_attn
+      value: [0, 0.5, 0.3, 0.7, 1]
+    - filter: mlp
+      value: [1, 0.5, 0.7, 0.3, 0]
+    - value: 0.5
+dtype: bfloat16
+```

orig/config.json

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+{
+  "_name_or_path": "Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context",
+  "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": 32,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.35.2",
+  "use_cache": false,
+  "vocab_size": 32000
+}

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+slices:
+  - sources:
+      - model: Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context
+        layer_range: [0, 32]
+      - model: SanjiWatsuki/Kunoichi-DPO-v2-7B
+        layer_range: [0, 32]
+merge_method: slerp
+base_model: Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context
+parameters:
+  t:
+    - filter: self_attn
+      value: [0, 0.5, 0.3, 0.7, 1]
+    - filter: mlp
+      value: [1, 0.5, 0.7, 0.3, 0]
+    - value: 0.5
+dtype: bfloat16

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quant/README.md

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+
+Thanks to @s3nh for the great quantization notebook code.
+---
+license: openrail
+pipeline_tag: text-generation
+library_name: transformers
+language:
+- en
+---
+
+
+## Original model card
+
+Buy @s3nh a coffee if you like this project ;)
+<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
+
+#### Description
+
+GGUF Format model files for [This project](https://huggingface.co/Test157t/Kunocchini-7b-128k-test).
+
+### GGUF Specs
+
+GGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired:
+
+Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information.
+Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models.
+mmap compatibility: models can be loaded using mmap for fast loading and saving.
+Easy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used.
+Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user.
+The key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values.
+This allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for
+inference or for identifying the model.
+
+# Original model card
+

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