chore(model gallery): add liquidai_lfm2-350m-extract

gallery/index.yaml

@@ -267,7 +267,7 @@
       sha256: e83ba6e675b747f7801557dc24594f43c17a7850b6129d4972d55e3e9b010359
       uri: huggingface://bartowski/OpenGVLab_InternVL3_5-8B-GGUF/mmproj-OpenGVLab_InternVL3_5-2B-f16.gguf
 - &lfm2
-  url: "github:mudler/LocalAI/gallery/chatml.yaml@master"
+  url: "github:mudler/LocalAI/gallery/lfm.yaml@master"
   name: "lfm2-vl-450m"
   license: lfm1.0
   tags:
@@ -327,6 +327,28 @@
     - filename: LFM2-1.2B-F16.gguf
       sha256: 0ddedfb8c5f7f73e77f19678bbc0f6ba2554d0534dd0feea65ea5bca2907d5f2
       uri: huggingface://LiquidAI/LFM2-1.2B-GGUF/LFM2-1.2B-F16.gguf
+- !!merge <<: *lfm2
+  name: "liquidai_lfm2-350m-extract"
+  urls:
+    - https://huggingface.co/LiquidAI/LFM2-350M-Extract
+    - https://huggingface.co/bartowski/LiquidAI_LFM2-350M-Extract-GGUF
+  description: |
+    Based on LFM2-350M, LFM2-350M-Extract is designed to extract important information from a wide variety of unstructured documents (such as articles, transcripts, or reports) into structured outputs like JSON, XML, or YAML.
+    Use cases:
+
+        Extracting invoice details from emails into structured JSON.
+        Converting regulatory filings into XML for compliance systems.
+        Transforming customer support tickets into YAML for analytics pipelines.
+        Populating knowledge graphs with entities and attributes from unstructured reports.
+
+    You can find more information about other task-specific models in this blog post.
+  overrides:
+    parameters:
+      model: LiquidAI_LFM2-350M-Extract-Q4_K_M.gguf
+  files:
+    - filename: LiquidAI_LFM2-350M-Extract-Q4_K_M.gguf
+      sha256: 340a7fb24b98a7dbe933169dbbb869f4d89f8c7bf27ee45d62afabfc5b376743
+      uri: huggingface://bartowski/LiquidAI_LFM2-350M-Extract-GGUF/LiquidAI_LFM2-350M-Extract-Q4_K_M.gguf
 - name: "kokoro"
   url: "github:mudler/LocalAI/gallery/virtual.yaml@master"
   urls:
@@ -2792,7 +2814,7 @@
     - gemma3
     - gemma-3
   overrides:
     #mmproj: gemma-3-27b-it-mmproj-f16.gguf
     parameters:
       model: gemma-3-27b-it-Q4_K_M.gguf
   files:
@@ -2810,7 +2832,7 @@
   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:
@@ -2828,7 +2850,7 @@
   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:
@@ -6373,7 +6395,7 @@
       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

gallery/lfm.yaml

@@ -0,0 +1,46 @@
+---
+name: "lfm"
+
+config_file: |
+  backend: "llama-cpp"
+  mmap: true
+  template:
+    chat_message: |
+      <|im_start|>{{ .RoleName }}
+      {{ if .FunctionCall -}}
+      <|tool_call_start|>
+      {{ else if eq .RoleName "tool" -}}
+      <|tool_response_start|>
+      {{ end -}}
+      {{ if .Content -}}
+      {{.Content }}
+      {{ end -}}
+      {{ if eq .RoleName "tool" -}}
+      <|tool_response_end|>
+      {{ end -}}
+      {{ if .FunctionCall -}}
+      {{toJson .FunctionCall}}
+      {{ end -}}<|im_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.
+      List of tools: <|tool_list_start|>[
+      {{range .Functions}}
+      {'type': 'function', 'function': {'name': '{{.Name}}', 'description': '{{.Description}}', 'parameters': {{toJson .Parameters}} }}
+      {{end}}
+      ]<|tool_list_end|>
+      <|im_end|>
+      {{.Input -}}
+      <|im_start|>assistant
+    chat: |
+      {{.Input -}}
+      <|im_start|>assistant
+    completion: |
+      {{.Input}}
+  context_size: 4096
+  f16: true
+  stopwords:
+  - '<|im_end|>'
+  - '<dummy32000>'
+  - '</s>'
+  - '<|endoftext|>'