chore(model gallery): add liquidai_lfm2-350m-extract (#6387)

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

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:

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|>'