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README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- 32 bit upscale
+- full 32 bit precision
+- master files
+---
+<h3> Master Files for Ultra High Quality Remasters of "Psyonic-Cetacean" 20B </h3>
+
+<img src="science-mad.jpg" width=300px height=300px style="float:right; padding:5px;">
+
+May "Space Whale" swim in the oceans of the universe forever!
+
+This repo contains the full precision (32 bit) master files for 32 bit upscales created by "DavidAU" of:
+
+https://huggingface.co/DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF-imatrix
+
+And
+
+https://huggingface.co/DavidAU/Psyonic-Cetacean-Ultra-Quality-20b-GGUF
+
+Please view either repo for details on the remaster's results, and other important infomation.
+
+<font color="red"><B>IMPORTANT NOTES For Maximum Results: </b></font>
+
+These are "final" result files of the full precision rebuild (including end result merge(s)) minus 
+GGUF and Imatrix level upscaling / adjustments which occuring during "GGUFing" processes.
+
+If you use these to create your own GGUFs, please use "outfile" at F32 for best results. If
+you use F16 this will reduce the quality by a factor of 2 or higher.
+
+Imatrix processes should use a stable dataset(s) of at least 500 "chunks" or more. 
+If smaller dataset(s) are used this may corrupt or reduce the quality of the Imatrix builds.
+
+Due to the precision remaster there will be "greater" distance between each quant - both
+non imatrix and imatrix.
+
+IE: The jump in quality, instruction following, "ai brainpower", nuance and output
+between Q4 and Q5 and likewise Q5 and Q6 will be larger than normal.
+
+Same applies to "Imatrix" quants.
+
+In addition there will also be differences between exact Imatrix and non-imatrix quants
+especially in terms of "creative uses" and/or uses where there is no "right answer".
+
+Finally, in terms of prompts:
+
+You may find longer prompts are no longer required and/or you may need to reduce the size
+of prompts in usage. This is a factor due to the precision upscale.
+
+Doing this will ensure the quality of the upscale is maximized in the GGUFs.
+
+/* GPTQers: 
+
+Suggest 4bit-Act32 TRUE for best results.
+
+/* EXL2ers: 
+
+Suggest Min 4.5 BPW or higher ; 6 BPW and up is especially potent. 
+Strongly suggest you do not reduce layer bit count, as this will affect depth and nuance.
+The more BPW the better.
+
+Happy GGUFing, EXL2ing, GPTQing, AWQing, HQQing and of course "Merging".
+
+<b>LONG LIVE OPEN SOURCE!</B>
+
+<I>DavidAU</I>
+
+/* Drop me a note when up, and I will link the masters to your repos.

added_tokens.json

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+{
+  "</s>": 2,
+  "<s>": 1,
+  "<unk>": 0
+}

config.json

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+{
+    "_name_or_path": "F:/13B/Orca-2-13b_flat",
+    "architectures": [
+        "LlamaForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 1,
+    "eos_token_id": 2,
+    "hidden_act": "silu",
+    "hidden_size": 5120,
+    "initializer_range": 0.02,
+    "intermediate_size": 13824,
+    "max_position_embeddings": 4096,
+    "mlp_bias": false,
+    "model_type": "llama",
+    "num_attention_heads": 40,
+    "num_hidden_layers": 62,
+    "num_key_value_heads": 40,
+    "pad_token_id": 0,
+    "pretraining_tp": 1,
+    "rms_norm_eps": 1e-05,
+    "rope_scaling": null,
+    "rope_theta": 10000.0,
+    "tie_word_embeddings": false,
+    "torch_dtype": "float32",
+    "transformers_version": "4.42.0.dev0",
+    "use_cache": true,
+    "vocab_size": 32000,
+    "quantization_config": {
+        "quant_method": "exl2",
+        "version": "0.1.3",
+        "bits": 2.5,
+        "head_bits": 6,
+        "calibration": {
+            "rows": 100,
+            "length": 2048,
+            "dataset": "(default)"
+        }
+    }
+}

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