chore(models): add gpt-oss-20b

gallery/harmony.yaml

@@ -0,0 +1,76 @@
+---
+name: "harmony"
+
+config_file: |
+  mmap: true
+  backend: "llama-cpp"
+  template:
+    chat_message: |
+      <|start|>{{ if .FunctionCall -}}functions.{{ .FunctionCall.Name }} to=assistant{{ else if eq .RoleName "assistant"}}<|channel|>final<|message|>{{else}}{{ .RoleName }}{{end}}<|message|>
+      {{ if .Content -}}
+      {{.Content }}
+      {{ end -}}
+      {{ if .FunctionCall -}}
+      {{toJson .FunctionCall}}
+      {{ end -}}<|end|>
+    function: |
+      <|im_start|>system
+      You are a function calling AI model. You are provided with functions to execute. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools:
+
+      <|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.
+      Knowledge cutoff: 2024-06
+      Current date: {{ now | date }}
+
+      Reasoning: medium
+
+      # Tools
+
+      ## functions
+
+      namespace functions {
+      {{-range .Functions}}
+      {{if .Description }}
+      // {{ .Description }}
+      {{- end }}
+      {{- if and .Parameters.Properties (gt (len .Parameters.Properties) 0) }}
+      type {{ .Name }} = (_: {
+      {{- range $name, $prop := .Parameters.Properties }}
+      {{- if $prop.Description }}
+        // {{ $prop.Description }}
+      {{- end }}
+        {{ $name }}: {{ if gt (len $prop.Type) 1 }}{{ range $i, $t := $prop.Type }}{{ if $i }} | {{ end }}{{ $t }}{{ end }}{{ else }}{{ index $prop.Type 0 }}{{ end }},
+      {{- end }}
+      }) => any;
+      {{- else }}
+      type {{ .Function.Name }} = () => any;
+      {{- end }}
+      {{- end }}{{/* end of range .Functions */}}
+      } // namespace functions
+
+      # Instructions
+
+      <|end|>
+      {{.Input -}}
+      <|start|>assistant
+    chat: |
+      <|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.
+      Knowledge cutoff: 2024-06
+      Current date: {{ now | date }}
+
+      Reasoning: medium
+
+      # Instructions
+
+      <|end|>
+      {{.Input -}}
+      <|im_start|>assistant
+    completion: |
+      {{.Input}}
+  context_size: 8192
+  f16: true
+  stopwords:
+  - '<|im_end|>'
+  - '<dummy32000>'
+  - '</s>'
+  - '<|endoftext|>'
+  - '<|end|>s'

gallery/index.yaml

@@ -1,4 +1,45 @@
 ---
+- &gptoss
+  name: "gpt-oss-20b"
+  url: "github:mudler/LocalAI/gallery/harmony.yaml@master"
+  license: apache-2.0
+  tags:
+    - gguf
+    - gpu
+    - cpu
+    - gguf
+    - openai
+  icon: https://raw.githubusercontent.com/openai/gpt-oss/main/docs/gpt-oss-20b.svg
+  urls:
+    - https://huggingface.co/openai/gpt-oss-20b
+    - https://huggingface.co/ggml-org/gpt-oss-20b-GGUF
+  description: |
+    Welcome to the gpt-oss series, OpenAI’s open-weight models designed for powerful reasoning, agentic tasks, and versatile developer use cases.
+
+    We’re releasing two flavors of the open models:
+
+        gpt-oss-120b — for production, general purpose, high reasoning use cases that fits into a single H100 GPU (117B parameters with 5.1B active parameters)
+        gpt-oss-20b — for lower latency, and local or specialized use cases (21B parameters with 3.6B active parameters)
+
+    Both models were trained on our harmony response format and should only be used with the harmony format as it will not work correctly otherwise.
+
+        This model card is dedicated to the smaller gpt-oss-20b model. Check out gpt-oss-120b for the larger model.
+
+    Highlights
+
+        Permissive Apache 2.0 license: Build freely without copyleft restrictions or patent risk—ideal for experimentation, customization, and commercial deployment.
+        Configurable reasoning effort: Easily adjust the reasoning effort (low, medium, high) based on your specific use case and latency needs.
+        Full chain-of-thought: Gain complete access to the model’s reasoning process, facilitating easier debugging and increased trust in outputs. It’s not intended to be shown to end users.
+        Fine-tunable: Fully customize models to your specific use case through parameter fine-tuning.
+        Agentic capabilities: Use the models’ native capabilities for function calling, web browsing, Python code execution, and Structured Outputs.
+        Native MXFP4 quantization: The models are trained with native MXFP4 precision for the MoE layer, making gpt-oss-120b run on a single H100 GPU and the gpt-oss-20b model run within 16GB of memory.
+  overrides:
+    parameters:
+      model: gpt-oss-20b-mxfp4.gguf
+  files:
+    - filename: gpt-oss-20b-mxfp4.gguf
+      sha256: 52f57ab7d3df3ba9173827c1c6832e73375553a846f3e32b49f1ae2daad688d4
+      uri: huggingface://ggml-org/gpt-oss-20b-GGUF/gpt-oss-20b-mxfp4.gguf
 - &afm
   name: "arcee-ai_afm-4.5b"
   url: "github:mudler/LocalAI/gallery/chatml.yaml@master"
@@ -1962,7 +2003,7 @@
     - gemma3
     - gemma-3
   overrides:
     #mmproj: gemma-3-27b-it-mmproj-f16.gguf
     parameters:
       model: gemma-3-27b-it-Q4_K_M.gguf
   files:
@@ -1980,7 +2021,7 @@
   description: |
     google/gemma-3-12b-it is an open-source, state-of-the-art, lightweight, multimodal model built from the same research and technology used to create the Gemini models. It is capable of handling text and image input and generating text output. It has a large context window of 128K tokens and supports over 140 languages. The 12B variant has been fine-tuned using the instruction-tuning approach. Gemma 3 models are suitable for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes them deployable in environments with limited resources such as laptops, desktops, or your own cloud infrastructure.
   overrides:
     #mmproj: gemma-3-12b-it-mmproj-f16.gguf
     parameters:
       model: gemma-3-12b-it-Q4_K_M.gguf
   files:
@@ -1998,7 +2039,7 @@
   description: |
     Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. Gemma-3-4b-it is a 4 billion parameter model.
   overrides:
     #mmproj: gemma-3-4b-it-mmproj-f16.gguf
     parameters:
       model: gemma-3-4b-it-Q4_K_M.gguf
   files:
@@ -5464,7 +5505,7 @@
       sha256: 2756551de7d8ff7093c2c5eec1cd00f1868bc128433af53f5a8d434091d4eb5a
       uri: huggingface://Triangle104/Nano_Imp_1B-Q8_0-GGUF/nano_imp_1b-q8_0.gguf
 - &qwen25
   name: "qwen2.5-14b-instruct" ## Qwen2.5
   icon: https://avatars.githubusercontent.com/u/141221163
   url: "github:mudler/LocalAI/gallery/chatml.yaml@master"
   license: apache-2.0
@@ -7425,7 +7466,7 @@
       sha256: 0fec82625f74a9a340837de7af287b1d9042e5aeb70cda2621426db99958b0af
       uri: huggingface://bartowski/Chuluun-Qwen2.5-72B-v0.08-GGUF/Chuluun-Qwen2.5-72B-v0.08-Q4_K_M.gguf
 - &smollm
   url: "github:mudler/LocalAI/gallery/chatml.yaml@master" ## SmolLM
   name: "smollm-1.7b-instruct"
   icon: https://huggingface.co/datasets/HuggingFaceTB/images/resolve/main/banner_smol.png
   tags: