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✨enhancement: Model sources should not be hard coded from frontend (#5270)
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‎extensions/model-extension/resources/default.json

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Original file line numberDiff line numberDiff line change
@@ -1,4 +1,187 @@
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[
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{
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"author": "Menlo",
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"id": "Menlo/Jan-nano",
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"metadata": {
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"_id": "68492cd9cada68b1d11ca1bd",
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"author": "Menlo",
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"cardData": {
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"license": "apache-2.0",
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"pipeline_tag": "text-generation"
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},
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"createdAt": "2025-06-11T07:14:33.000Z",
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"description": "---\nlicense: apache-2.0\npipeline_tag: text-generation\n---\n# Jan Nano\n\n\n\n![image/png](https://cdn-uploads.huggingface.co/production/uploads/657a81129ea9d52e5cbd67f7/YQci8jiHjAAFpXWYOadrU.png)\n\n## Overview\n\nJan Nano is a fine-tuned language model built on top of the Qwen3 architecture. Developed as part of the Jan ecosystem, it balances compact size and extended context length, making it ideal for efficient, high-quality text generation in local or embedded environments.\n\n## Features\n\n- **Tool Use**: Excellent function calling and tool integration\n- **Research**: Enhanced research and information processing capabilities\n- **Small Model**: VRAM efficient for local deployment\n\n## Use it with Jan (UI)\n\n1. Install **Jan** using [Quickstart](https://jan.ai/docs/quickstart)",
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"disabled": false,
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"downloads": 1434,
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"gated": false,
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"gguf": {
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"architecture": "qwen3",
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"bos_token": "<|endoftext|>",
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"chat_template": "{%- if tools %} {{- '<|im_start|>system\\n' }} {%- if messages[0].role == 'system' %} {{- messages[0].content + '\\n\\n' }} {%- endif %} {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }} {%- for tool in tools %} {{- \"\\n\" }} {{- tool | tojson }} {%- endfor %} {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }} {%- else %} {%- if messages[0].role == 'system' %} {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }} {%- endif %} {%- endif %} {%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %} {%- for message in messages[::-1] %} {%- set index = (messages|length - 1) - loop.index0 %} {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %} {%- set ns.multi_step_tool = false %} {%- set ns.last_query_index = index %} {%- endif %} {%- endfor %} {%- for message in messages %} {%- if message.content is string %} {%- set content = message.content %} {%- else %} {%- set content = '' %} {%- endif %} {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %} {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }} {%- elif message.role == \"assistant\" %} {%- set reasoning_content = '' %} {%- if message.reasoning_content is string %} {%- set reasoning_content = message.reasoning_content %} {%- else %} {%- if '</think>' in content %} {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %} {%- set content = content.split('</think>')[-1].lstrip('\\n') %} {%- endif %} {%- endif %} {%- if loop.index0 > ns.last_query_index %} {%- if loop.last or (not loop.last and reasoning_content) %} {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }} {%- else %} {{- '<|im_start|>' + message.role + '\\n' + content }} {%- endif %} {%- else %} {{- '<|im_start|>' + message.role + '\\n' + content }} {%- endif %} {%- if message.tool_calls %} {%- for tool_call in message.tool_calls %} {%- if (loop.first and content) or (not loop.first) %} {{- '\\n' }} {%- endif %} {%- if tool_call.function %} {%- set tool_call = tool_call.function %} {%- endif %} {{- '<tool_call>\\n{\"name\": \"' }} {{- tool_call.name }} {{- '\", \"arguments\": ' }} {%- if tool_call.arguments is string %} {{- tool_call.arguments }} {%- else %} {{- tool_call.arguments | tojson }} {%- endif %} {{- '}\\n</tool_call>' }} {%- endfor %} {%- endif %} {{- '<|im_end|>\\n' }} {%- elif message.role == \"tool\" %} {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %} {{- '<|im_start|>user' }} {%- endif %} {{- '\\n<tool_response>\\n' }} {{- content }} {{- '\\n</tool_response>' }} {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %} {{- '<|im_end|>\\n' }} {%- endif %} {%- endif %} {%- endfor %} {%- if add_generation_prompt %} {{- '<|im_start|>assistant\\n' }} {{- '<think>\\n\\n</think>\\n\\n' }} {%- endif %}",
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"context_length": 40960,
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"eos_token": "<|im_end|>",
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"quantize_imatrix_file": "imatrix.dat",
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"total": 4022468096
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},
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"id": "Menlo/Jan-nano",
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"lastModified": "2025-06-13T16:57:55.000Z",
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"likes": 3,
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"model-index": null,
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"modelId": "Menlo/Jan-nano",
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"pipeline_tag": "text-generation",
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"private": false,
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"sha": "a04aab0878648d8f284c63a52664a482ead16f06",
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"siblings": [
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{
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"rfilename": ".gitattributes",
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"size": 3460
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},
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{
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"rfilename": "README.md",
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"size": 776
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},
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{
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"rfilename": "jan-nano-4b-iQ4_XS.gguf",
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"size": 2270750400
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},
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{
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"rfilename": "jan-nano-4b-Q3_K_L.gguf",
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"size": 2239784384
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},
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{
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"rfilename": "jan-nano-4b-Q3_K_M.gguf",
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"size": 2075616704
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},
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{
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"rfilename": "jan-nano-4b-Q3_K_S.gguf",
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"size": 1886995904
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},
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{
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"rfilename": "jan-nano-4b-Q4_0.gguf",
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"size": 2369545664
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},
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{
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"rfilename": "jan-nano-4b-Q4_1.gguf",
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"size": 2596627904
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},
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{
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"rfilename": "jan-nano-4b-Q4_K_M.gguf",
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"size": 2497279424
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},
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{
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"rfilename": "jan-nano-4b-Q4_K_S.gguf",
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"size": 2383308224
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},
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{
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"rfilename": "jan-nano-4b-Q5_0.gguf",
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"size": 2823710144
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},
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{
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"rfilename": "jan-nano-4b-Q5_1.gguf",
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"size": 3050792384
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},
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{
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"rfilename": "jan-nano-4b-Q5_K_M.gguf",
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"size": 2889512384
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},
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{
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"rfilename": "jan-nano-4b-Q5_K_S.gguf",
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"size": 2823710144
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},
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{
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"rfilename": "jan-nano-4b-Q6_K.gguf",
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"size": 3306259904
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},
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{
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"rfilename": "jan-nano-4b-Q8_0.gguf",
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"size": 4280403904
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}
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],
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"spaces": [],
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"tags": [
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"gguf",
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"text-generation",
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"license:apache-2.0",
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"endpoints_compatible",
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"region:us",
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"imatrix",
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"conversational"
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],
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"usedStorage": 93538518464,
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"widgetData": [
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{
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"text": "Hi, what can you help me with?"
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},
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{
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"text": "What is 84 * 3 / 2?"
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},
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{
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"text": "Tell me an interesting fact about the universe!"
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},
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{
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"text": "Explain quantum computing in simple terms."
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}
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]
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},
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"models": [
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{
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"id": "Menlo:Jan-nano:jan-nano-4b-iQ4_XS.gguf",
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"size": 2270750400
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},
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{
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"id": "Menlo:Jan-nano:jan-nano-4b-Q3_K_L.gguf",
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"size": 2239784384
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},
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{
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"id": "Menlo:Jan-nano:jan-nano-4b-Q3_K_M.gguf",
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"size": 2075616704
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},
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{
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"id": "Menlo:Jan-nano:jan-nano-4b-Q3_K_S.gguf",
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"size": 1886995904
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},
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{
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"id": "Menlo:Jan-nano:jan-nano-4b-Q4_0.gguf",
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"size": 2369545664
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},
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{
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"id": "Menlo:Jan-nano:jan-nano-4b-Q4_1.gguf",
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"size": 2596627904
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},
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{
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"id": "Menlo:Jan-nano:jan-nano-4b-Q4_K_M.gguf",
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"size": 2497279424
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},
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{
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"id": "Menlo:Jan-nano:jan-nano-4b-Q4_K_S.gguf",
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"size": 2383308224
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},
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{
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"id": "Menlo:Jan-nano:jan-nano-4b-Q5_0.gguf",
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"size": 2823710144
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},
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{
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"id": "Menlo:Jan-nano:jan-nano-4b-Q5_1.gguf",
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"size": 3050792384
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},
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{
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"id": "Menlo:Jan-nano:jan-nano-4b-Q5_K_M.gguf",
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"size": 2889512384
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},
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{
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"id": "Menlo:Jan-nano:jan-nano-4b-Q5_K_S.gguf",
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"size": 2823710144
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},
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{
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"id": "Menlo:Jan-nano:jan-nano-4b-Q6_K.gguf",
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"size": 3306259904
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},
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{
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"id": "Menlo:Jan-nano:jan-nano-4b-Q8_0.gguf",
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"size": 4280403904
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}
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]
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},
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{
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"author": "PrimeIntellect",
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"id": "cortexso/intellect-2",

‎extensions/model-extension/src/index.ts

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@@ -25,6 +25,11 @@ type Data<T> = {
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data: T[]
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}
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/**
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* Defaul mode sources
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*/
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const defaultModelSources = ['Menlo/Jan-nano']
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/**
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* A extension for models
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*/
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fetchModelsHub = async () => {
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const models = await this.fetchModels()
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defaultModelSources.forEach((model) => {
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this.addSource(model).catch((e) => {
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console.debug(`Failed to add default model source ${model}:`, e)
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})
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})
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return this.apiInstance()
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.then((api) =>
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api
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.get('v1/models/hub?author=cortexso&tag=cortex.cpp')
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.json<Data<string>>()
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.then(async (e) => {
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await Promise.all(
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e.data?.map((model) => {
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[...(e.data ?? []), ...defaultModelSources].map((model) => {
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if (
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!models.some(
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(e) => 'modelSource' in e && e.modelSource === model

‎web-app/src/services/models.ts

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Original file line numberDiff line numberDiff line change
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/* eslint-disable @typescript-eslint/no-explicit-any */
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import { ExtensionManager } from '@/lib/extension'
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import { normalizeProvider } from '@/lib/models'
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import { hardcodedModel } from '@/utils/models'
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import { EngineManager, ExtensionTypeEnum, ModelExtension } from '@janhq/core'
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import { Model as CoreModel } from '@janhq/core'
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ExtensionTypeEnum.Model
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)
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if (!extension) return [hardcodedModel]
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if (!extension) return []
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try {
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const sources = await extension.getSources()
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}))
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// Prepend the hardcoded model to the sources
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return [hardcodedModel, ...mappedSources]
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return [...mappedSources]
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} catch (error) {
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console.error('Failed to fetch model sources:', error)
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return [hardcodedModel]
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return []
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}
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}
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?.fetchModelsHub()
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// Prepend the hardcoded model to the hub data
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return hubData ? [hardcodedModel, ...hubData] : [hardcodedModel]
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return hubData ? [...hubData] : []
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}
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/**

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