| FROM auralexity.gguf | |
| SYSTEM """ | |
| You are an AI model named Auralexity, you are based on Gemma3 and fine-tuned by NeuralNexusLab. | |
| """ | |
| LICENSE """ | |
| MIT License | |
| Copyright (c) 2025 NeuralNexusLab | |
| Permission is hereby granted, free of charge, to any person obtaining a copy | |
| of this software and associated documentation files (the "Software"), to deal | |
| in the Software without restriction, including without limitation the rights | |
| to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
| copies of the Software, and to permit persons to whom the Software is | |
| furnished to do so, subject to the following conditions: | |
| The above copyright notice and this permission notice shall be included in all | |
| copies or substantial portions of the Software. | |
| THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
| IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
| FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | |
| AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
| LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | |
| OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | |
| SOFTWARE. | |
| Note: Usage of the Auralexity model weights must also comply with the underlying | |
| foundational model licenses (Google Gemma Terms of Use). | |
| """ | |
| TEMPLATE """{{- range $i, $_ := .Messages }} | |
| {{- $last := eq (len (slice $.Messages $i)) 1 }} | |
| {{- if or (eq .Role "user") (eq .Role "system") }}<start_of_turn>user | |
| {{ .Content }}<end_of_turn> | |
| {{ if $last }}<start_of_turn>model | |
| {{ end }} | |
| {{- else if eq .Role "assistant" }}<start_of_turn>model | |
| {{ .Content }}{{ if not $last }}<end_of_turn> | |
| {{ end }} | |
| {{- end }} | |
| {{- end }}""" | |
| PARAMETER stop "<end_of_turn>" | |
| PARAMETER stop "<eos>" | |
| PARAMETER temperature 0.1 | |
| PARAMETER min_p 0.0 | |
| PARAMETER top_k 64 | |
| PARAMETER top_p 0.95 | |
| PARAMETER num_predict 32768 |