Upload folder
Browse files- .gitattributes +1 -0
- blobs/3009e23970737db921277af66eb208b6b1e8702f +146 -0
- blobs/3709311b8b7d4f014acc4d46d1d59ac7189a4890 +985 -0
- blobs/48d0950630d6c8df3a96de2e444abbaddd74ea2d +317 -0
- blobs/4a5388286b92df802c1599a1a2cfcd4fa44fafb6 +0 -0
- blobs/53302b5a885b0702390a64f6590481a1d9ae023b +39 -0
- blobs/580d310c2d211aca166288a207e972ced9bb0100 +1 -0
- blobs/6c83b16a6412e25a7bae459daf7e93466c885093 +78 -0
- blobs/962c4ff7160f8765f4a0a2a1f96c465cba6d8d8e +9 -0
- blobs/a5ad94eb2fc98b41bd89b8cf33f446e86c472ccd15382c0f17a53028af90258d.incomplete +0 -0
- blobs/d74f9032fc644874e6fe9280be84d4e7b0907939 +0 -0
- blobs/de6aa5e4ad061bbe8400d0a92bb2243a59b3dc49 +65 -0
- blobs/ede21e29cb7086f12a84eca01cda0a448e368772 +23 -0
- blobs/f00e0ca743748c8a900d937f9b7a2f043a39ae13 +41 -0
- blobs/f78e77184ab4f3d53e28b85ffda8b328185cc40fcfaab59d17c3df0f2524621e.incomplete +3 -0
.gitattributes
CHANGED
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@@ -64,3 +64,4 @@ blobs/39ab057316af49c3d81c67b80a98d72727ce686ac68ae72ce71a05fc5297b856 filter=lf
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| 64 |
blobs/e1219ef85875905368b39e3fe383d72fc6539ade5abf81f7cedf94a19275a345 filter=lfs diff=lfs merge=lfs -text
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| 65 |
blobs/9ce20192fbe0d521d100521f1e0836c415debacb615b89f7658178420822e710 filter=lfs diff=lfs merge=lfs -text
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blobs/1fa4abb1ce00765aa78c4714c71c65c57e46706564aa8f908e78d7c6fa51d07e filter=lfs diff=lfs merge=lfs -text
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| 64 |
blobs/e1219ef85875905368b39e3fe383d72fc6539ade5abf81f7cedf94a19275a345 filter=lfs diff=lfs merge=lfs -text
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| 65 |
blobs/9ce20192fbe0d521d100521f1e0836c415debacb615b89f7658178420822e710 filter=lfs diff=lfs merge=lfs -text
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blobs/1fa4abb1ce00765aa78c4714c71c65c57e46706564aa8f908e78d7c6fa51d07e filter=lfs diff=lfs merge=lfs -text
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| 67 |
+
blobs/f78e77184ab4f3d53e28b85ffda8b328185cc40fcfaab59d17c3df0f2524621e.incomplete filter=lfs diff=lfs merge=lfs -text
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blobs/3009e23970737db921277af66eb208b6b1e8702f
ADDED
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@@ -0,0 +1,146 @@
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| 1 |
+
{%- if not add_generation_prompt is defined %}
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| 2 |
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{%- set add_generation_prompt = false %}
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| 3 |
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{%- endif %}
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| 4 |
+
{%- set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='', is_first_sp=true, is_first_user=true, is_last_user=false) %}
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| 5 |
+
{%- for message in messages %}
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| 6 |
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{%- if message['role'] == 'system' %}
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| 7 |
+
{%- if ns.is_first_sp %}
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| 8 |
+
{%- set ns.system_prompt = ns.system_prompt + message['content'] %}
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| 9 |
+
{%- set ns.is_first_sp = false %}
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| 10 |
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{%- else %}
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| 11 |
+
{% set ns.system_prompt = ns.system_prompt + '\n\n' + message['content'] %}
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| 12 |
+
{%- endif %}
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| 13 |
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{%- endif %}
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| 14 |
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{%- endfor %}
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| 15 |
+
{{- bos_token }}
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{{- ns.system_prompt }}
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| 17 |
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{%- if tools %}
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| 18 |
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{%- if ns.system_prompt != '' %}
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| 19 |
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{{- '\n\n# Tools\n\nYou may call one or more functions to assist with the user query.' }}
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| 20 |
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{%- else %}
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| 21 |
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{{- '# Tools\n\nYou may call one or more functions to assist with the user query.' }}
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| 22 |
+
{%- endif %}
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| 23 |
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{{- '\n\nYou are provided with function signatures within <tools></tools> XML tags:' }}
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| 24 |
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{{- '\n<tools>\n' }}
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| 25 |
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{%- for tool in tools %}
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| 26 |
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{%- if loop.index0 > 1 %}
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| 27 |
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{{- '\n' }}
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| 28 |
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{%- endif %}
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| 29 |
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{{- tool | tojson }}
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| 30 |
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{%- endfor %}
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| 31 |
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{{- '\n</tools>\n\n' }}
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| 32 |
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{{- 'For function call returns, you should first print <tool_calls>' }}
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| 33 |
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{{- 'For each function call, you should return object like:\n' }}
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| 34 |
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{{- '<tool_call>function_name\n```json\nfunction_arguments_in_json_format\n```</tool_call>' }}
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| 35 |
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{{- 'At the end of function call returns, you should print </tool_calls>' }}
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| 36 |
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{%- endif %}
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| 37 |
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{%- if ns.system_prompt != '' or tools %}
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| 38 |
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{{- '<｜hy_place▁holder▁no▁3｜>' }}
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| 39 |
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{%- endif %}
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| 40 |
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{%- set image_count = namespace(value=0) %}
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| 41 |
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{%- set video_count = namespace(value=0) %}
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| 42 |
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{%- set last_user_idx = namespace(value=-1) %}
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| 43 |
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{%- for message in messages %}
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| 44 |
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{%- if message['role'] == 'user' %}
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| 45 |
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{%- set last_user_idx.value = loop.index0 %}
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{%- endif %}
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{%- endfor %}
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| 48 |
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{%- for message in messages %}
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{%- if message['role'] == 'user' %}
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| 50 |
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{%- set ns.is_tool = false %}
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| 51 |
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{%- set ns.is_first = false %}
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| 52 |
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{%- set ns.is_last_user = true %}
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| 53 |
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{{- '<｜hy_User｜>'}}
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| 54 |
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{%- if message.content is string %}
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| 55 |
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{{- message.content }}
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| 56 |
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{%- else %}
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| 57 |
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{%- for content in message.content %}
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| 58 |
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{%- if content.type == 'image' or 'image' in content or 'image_url' in content %}
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| 59 |
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{%- set image_count.value = image_count.value + 1 %}
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| 60 |
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{%- if add_vision_id %}Picture {{ image_count.value }}: {% endif -%}
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| 61 |
+
<｜hy_place▁holder▁no▁666｜><｜hy_place▁holder▁no▁669｜><｜hy_place▁holder▁no▁672｜><｜hy_place▁holder▁no▁667｜>
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| 62 |
+
{%- elif content.type == 'video' or 'video' in content %}
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| 63 |
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{%- set video_count.value = video_count.value + 1 %}
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| 64 |
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{%- if add_vision_id %}Video {{ video_count.value }}: {% endif -%}
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| 65 |
+
<｜hy_place▁holder▁no▁666｜><｜hy_place▁holder▁no▁670｜><｜hy_place▁holder▁no▁672｜><｜hy_place▁holder▁no▁667｜>
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| 66 |
+
{%- elif 'text' in content %}
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| 67 |
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{{- content.text }}
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| 68 |
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{%- endif %}
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| 69 |
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{%- endfor %}
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| 70 |
+
{%- endif %}
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| 71 |
+
{%- if loop.index0 == last_user_idx.value %}
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| 72 |
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{%- if enable_thinking is defined and enable_thinking %}
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| 73 |
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{{- '/think' }}
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| 74 |
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{%- else %}
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| 75 |
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{{- '/no_think' }}
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| 76 |
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{%- endif %}
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| 77 |
+
{%- else %}
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| 78 |
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{{- '/no_think' }}
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| 79 |
+
{%- endif %}
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| 80 |
+
{%- endif %}
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| 81 |
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{%- if message['role'] == 'assistant' and message['tool_calls'] is defined and message['tool_calls'] is not none %}
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| 82 |
+
{%- set ns.is_last_user = false %}
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| 83 |
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{%- if ns.is_tool %}
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| 84 |
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{{- '</tool_responses>' }}
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| 85 |
+
{%- endif %}
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| 86 |
+
{{- '<｜hy_Assistant｜>' }}
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| 87 |
+
{%- set ns.is_first = false %}
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| 88 |
+
{%- set ns.is_tool = false %}
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| 89 |
+
{%- set ns.is_output_first = true %}
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| 90 |
+
{%- for tool in message['tool_calls'] %}
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| 91 |
+
{%- set arguments = tool['function']['arguments'] %}
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| 92 |
+
{%- if arguments is not string %}
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| 93 |
+
{%- set arguments = arguments | tojson %}
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| 94 |
+
{%- endif %}
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| 95 |
+
{%- if not ns.is_first %}
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| 96 |
+
{%- if message['content'] is none %}
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| 97 |
+
{{- '<tool_calls><tool_call>' + tool['function']['name'] + '\n```json\n' + arguments + '\n```</tool_call>' }}
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| 98 |
+
{%- else %}
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| 99 |
+
{{- message['content'] + '<tool_calls><tool_call>' + tool['function']['name'] + '\n```json\n' + arguments + '\n```</tool_call>' }}
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| 100 |
+
{%- endif %}
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| 101 |
+
{%- set ns.is_first = true %}
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| 102 |
+
{%- else %}
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| 103 |
+
{{- '\n<tool_call>' + tool['function']['name'] + '\n```json\n' + arguments + '\n```</tool_call>' }}
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| 104 |
+
{%- endif %}
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| 105 |
+
{%- endfor %}
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| 106 |
+
{{- '</tool_calls>' + eos_token }}
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| 107 |
+
{%- endif %}
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| 108 |
+
{%- if message['role'] == 'assistant' and (message['tool_calls'] is not defined or message['tool_calls'] is none) %}
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| 109 |
+
{%- set ns.is_last_user = false %}
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| 110 |
+
{{- '<｜hy_Assistant｜>' }}
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| 111 |
+
{{- '<think>\n\n</think>\n' }}
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| 112 |
+
{{- '<answer>\n' }}
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| 113 |
+
{%- if message['content'] is string %}
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| 114 |
+
{{- message['content'] }}
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| 115 |
+
{%- else %}
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| 116 |
+
{%- for content_item in message['content'] %}
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| 117 |
+
{%- if 'text' in content_item %}
|
| 118 |
+
{{- content_item['text'] }}
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| 119 |
+
{%- endif %}
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| 120 |
+
{%- endfor %}
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| 121 |
+
{%- endif %}
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| 122 |
+
{{- '\n</answer>' }}
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| 123 |
+
{{- eos_token }}
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| 124 |
+
{%- endif %}
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| 125 |
+
{%- if message['role'] == 'tool' %}
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| 126 |
+
{%- set ns.is_last_user = false %}
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| 127 |
+
{%- set ns.is_tool = true %}
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| 128 |
+
{%- if ns.is_output_first %}
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| 129 |
+
{{- '<｜hy_User｜>' + '<tool_responses><tool_response>' + message['content'] + '</tool_response>' }}
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| 130 |
+
{%- set ns.is_output_first = false %}
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| 131 |
+
{%- else %}
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| 132 |
+
{{- '\n<tool_response>' + message['content'] + '</tool_response>' }}
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| 133 |
+
{%- endif %}
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| 134 |
+
{%- endif %}
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| 135 |
+
{%- endfor %}
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| 136 |
+
{%- if ns.is_tool %}
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| 137 |
+
{{- '</tool_responses>' }}
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| 138 |
+
{%- endif %}
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| 139 |
+
{%- if add_generation_prompt %}
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| 140 |
+
{{- '<｜hy_Assistant｜>' }}
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| 141 |
+
{%- if enable_thinking is defined and enable_thinking %}
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| 142 |
+
{{- '<think>' }}
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| 143 |
+
{%- else %}
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| 144 |
+
{{- '<think>\n\n</think>\n' }}
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| 145 |
+
{%- endif %}
|
| 146 |
+
{%- endif %}
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blobs/3709311b8b7d4f014acc4d46d1d59ac7189a4890
ADDED
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@@ -0,0 +1,985 @@
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|
| 1 |
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{
|
| 2 |
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"metadata": {
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| 3 |
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|
| 4 |
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| 5 |
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"model.visual.vision_tower.blocks.9.mlp.fc2.weight": "model-00002-of-00002.safetensors",
|
| 977 |
+
"model.visual.vision_tower.blocks.9.norm1.bias": "model-00002-of-00002.safetensors",
|
| 978 |
+
"model.visual.vision_tower.blocks.9.norm1.weight": "model-00002-of-00002.safetensors",
|
| 979 |
+
"model.visual.vision_tower.blocks.9.norm2.bias": "model-00002-of-00002.safetensors",
|
| 980 |
+
"model.visual.vision_tower.blocks.9.norm2.weight": "model-00002-of-00002.safetensors",
|
| 981 |
+
"model.visual.vision_tower.patch_embed.proj.bias": "model-00002-of-00002.safetensors",
|
| 982 |
+
"model.visual.vision_tower.patch_embed.proj.weight": "model-00002-of-00002.safetensors",
|
| 983 |
+
"model.visual.vision_tower.pos_embed": "model-00002-of-00002.safetensors"
|
| 984 |
+
}
|
| 985 |
+
}
|
blobs/48d0950630d6c8df3a96de2e444abbaddd74ea2d
ADDED
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@@ -0,0 +1,317 @@
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|
| 1 |
+
---
|
| 2 |
+
license: other
|
| 3 |
+
language:
|
| 4 |
+
- multilingual
|
| 5 |
+
pipeline_tag: image-text-to-text
|
| 6 |
+
library_name: transformers
|
| 7 |
+
base_model:
|
| 8 |
+
- tencent/Hunyuan-Embodied-0.5
|
| 9 |
+
tags:
|
| 10 |
+
- hunyuan
|
| 11 |
+
- vision-language
|
| 12 |
+
- Embodied
|
| 13 |
+
- image-to-text
|
| 14 |
+
- 2B
|
| 15 |
+
- end-to-end
|
| 16 |
+
- MoT
|
| 17 |
+
---
|
| 18 |
+
<div align="center">
|
| 19 |
+
<h1>HY-Embodied</h1>
|
| 20 |
+
<p><b>A Family of Embodied Foundation Models for Real-World Agents</b></p>
|
| 21 |
+
<p><i>Tencent Robotics X × HY Vision Team</i></p>
|
| 22 |
+
|
| 23 |
+
<a href="https://github.com/Tencent-Hunyuan/HY-Embodied/blob/master/hy_embodied_tech_report.pdf"><img src="https://img.shields.io/badge/Paper-Report-red?logo=report" alt="Tech Report"></a>
|
| 24 |
+
<a href="https://arxiv.org/abs/2604.07430"><img src="https://img.shields.io/badge/Paper-Arxiv-red?logo=arxiv" alt="Paper"></a>
|
| 25 |
+
<a href="https://huggingface.co/tencent/HY-Embodied-0.5/tree/main"><img src="https://img.shields.io/badge/Models-HuggingFace-yellow?logo=huggingface" alt="Models"></a>
|
| 26 |
+
<a href="https://github.com/Tencent-Hunyuan/HY-Embodied"><img src="https://img.shields.io/badge/GitHub-Repo-181717?logo=github&logoColor=white" alt="GitHub"></a>
|
| 27 |
+
|
| 28 |
+
</div>
|
| 29 |
+
|
| 30 |
+
<div align="center">
|
| 31 |
+
<video src="https://github.com/user-attachments/assets/a5c6b872-2cb0-4f52-8321-894fee7da27e" controls autoplay muted loop width="85%"></video>
|
| 32 |
+
</div>
|
| 33 |
+
|
| 34 |
+
## 🔥 Updates
|
| 35 |
+
|
| 36 |
+
* **`[2026-04-09]`** 🚀 We have released **HY-Embodied-0.5**, featuring the open-sourced `HY-Embodied-0.5 MoT-2B` weights on [Hugging Face](https://huggingface.co/tencent/HY-Embodied-0.5/tree/main) along with the official inference code\!
|
| 37 |
+
|
| 38 |
+
## 📖 Abstract
|
| 39 |
+
|
| 40 |
+
We introduce **HY-Embodied-0.5**, a suite of foundation models tailored specifically for real-world embodied intelligence. To bridge the gap between general Vision-Language Models (VLMs) and the strict demands of physical agents, our models are engineered to excel in spatial-temporal visual perception and complex embodied reasoning (prediction, interaction, and planning).
|
| 41 |
+
|
| 42 |
+
The suite features an innovative **Mixture-of-Transformers (MoT)** architecture utilizing latent tokens for modality-specific computing, significantly enhancing fine-grained perception. It includes two primary variants: a highly efficient **2B model** for edge deployment and a powerful **32B model** for complex reasoning. Through a self-evolving post-training paradigm and large-to-small on-policy distillation, our compact MoT-2B outperforms state-of-the-art models of similar size across 16 benchmarks, while the 32B variant achieves frontier-level performance comparable to Gemini 3.0 Pro. Ultimately, HY-Embodied serves as a robust "brain" for Vision-Language-Action (VLA) pipelines, delivering compelling results in real-world physical robot control.
|
| 43 |
+
|
| 44 |
+
<div align="center">
|
| 45 |
+
<img src="https://github.com/Tencent-Hunyuan/HY-Embodied/blob/master/figures/teaser.png?raw=true" alt="HY-Embodied Teaser" width="85%">
|
| 46 |
+
</div>
|
| 47 |
+
|
| 48 |
+
|
| 49 |
+
## ⭐️ Key Features
|
| 50 |
+
|
| 51 |
+
* 🧠 **Evolved MoT Architecture:** Designed for maximum efficiency without sacrificing visual acuity. The MoT-2B variant contains 4B total parameters but requires **only 2.2B activated parameters** during inference. By emphasizing modality-specific computing in the vision pathway, it achieves the high inference speed of a dense 2B model while delivering superior, fine-grained perceptual representations.
|
| 52 |
+
* 🔗 **High-Quality Mixed Chain Reasoning:** We introduce an advanced iterative, self-evolving post-training pipeline. By employing on-policy distillation, we successfully transfer the sophisticated step-by-step reasoning, planning, and high-quality "thinking" capabilities from our powerful 32B model directly to the compact 2B variant.
|
| 53 |
+
* 🌍 **Large-Scale Embodied Pre-training:** Grounded in a massive, specially curated dataset comprising **\>100 million** embodied and spatial-specific data points. Trained on a corpus exceeding **200 billion tokens**, the model develops a deep, native understanding of 3D spaces, physical object interactions, and agent dynamics.
|
| 54 |
+
* 🦾 **Stronger VLA Application:** Beyond standard academic benchmarks, HY-Embodied is engineered to be the core cognitive engine for physical robots. It seamlessly integrates into Vision-Language-Action (VLA) frameworks, acting as a highly robust and capable brain to drive high success rates in complex, real-world robotic control tasks.
|
| 55 |
+
|
| 56 |
+
<div align="center">
|
| 57 |
+
<img src="https://github.com/Tencent-Hunyuan/HY-Embodied/blob/master/figures/arch.png?raw=true" alt="HY-Embodied Architecture" width="85%">
|
| 58 |
+
</div>
|
| 59 |
+
|
| 60 |
+
## 📅 Plannings
|
| 61 |
+
|
| 62 |
+
- [x] Transformers Inference
|
| 63 |
+
- [ ] vLLM Inference
|
| 64 |
+
- [ ] Online Gradio Demo
|
| 65 |
+
|
| 66 |
+
## 🛠️ Dependencies and Installation
|
| 67 |
+
|
| 68 |
+
### Prerequisites
|
| 69 |
+
|
| 70 |
+
- 🖥️ **Operating System**: Linux (recommended)
|
| 71 |
+
- 🐍 **Python**: 3.12+ (recommended and tested)
|
| 72 |
+
- ⚡ **CUDA**: 12.6
|
| 73 |
+
- 🔥 **PyTorch**: 2.8.0
|
| 74 |
+
- 🎮 **GPU**: NVIDIA GPU with CUDA support
|
| 75 |
+
|
| 76 |
+
### Installation
|
| 77 |
+
|
| 78 |
+
1. **Install the specific Transformers version required for this model:**
|
| 79 |
+
```bash
|
| 80 |
+
pip install git+https://github.com/huggingface/transformers@9293856c419762ebf98fbe2bd9440f9ce7069f1a
|
| 81 |
+
```
|
| 82 |
+
|
| 83 |
+
> **Note**: We will merge the improvements into the Transformers main branch later.
|
| 84 |
+
|
| 85 |
+
2. **Install other dependencies:**
|
| 86 |
+
```bash
|
| 87 |
+
pip install -r requirements.txt
|
| 88 |
+
```
|
| 89 |
+
|
| 90 |
+
### Quick Start
|
| 91 |
+
|
| 92 |
+
1. **Clone the repository:**
|
| 93 |
+
```bash
|
| 94 |
+
git clone https://github.com/Tencent-Hunyuan/HY-Embodied
|
| 95 |
+
cd HY-Embodied/
|
| 96 |
+
```
|
| 97 |
+
|
| 98 |
+
2. **Install dependencies:**
|
| 99 |
+
```bash
|
| 100 |
+
pip install -r requirements.txt
|
| 101 |
+
```
|
| 102 |
+
|
| 103 |
+
3. **Run inference:**
|
| 104 |
+
```bash
|
| 105 |
+
python inference.py
|
| 106 |
+
```
|
| 107 |
+
|
| 108 |
+
The example script demonstrates both single generation and batch generation capabilities.
|
| 109 |
+
|
| 110 |
+
### Model Download
|
| 111 |
+
|
| 112 |
+
The code automatically downloads the model `tencent/HY-Embodied-0.5` from Hugging Face Hub. Ensure you have sufficient disk space (8 GB) for the model weights.
|
| 113 |
+
|
| 114 |
+
### Hardware Requirements
|
| 115 |
+
|
| 116 |
+
- **GPU**: Recommended for optimal performance (NVIDIA GPU with at least 16GB VRAM)
|
| 117 |
+
- **CPU**: Supported but slower
|
| 118 |
+
- **Memory**: At least 16GB RAM recommended
|
| 119 |
+
- **Storage**: 20GB+ free space for model and dependencies
|
| 120 |
+
|
| 121 |
+
## 🚀 Quick Start with Transformers
|
| 122 |
+
|
| 123 |
+
### Basic Inference Example
|
| 124 |
+
|
| 125 |
+
```python
|
| 126 |
+
import os
|
| 127 |
+
import torch
|
| 128 |
+
from transformers import AutoModelForImageTextToText, AutoProcessor
|
| 129 |
+
|
| 130 |
+
# Load model & processor
|
| 131 |
+
MODEL_PATH = "tencent/HY-Embodied-0.5"
|
| 132 |
+
DEVICE = "cuda"
|
| 133 |
+
THINKING_MODE = False
|
| 134 |
+
TEMPERATURE = 0.8
|
| 135 |
+
|
| 136 |
+
processor = AutoProcessor.from_pretrained(MODEL_PATH)
|
| 137 |
+
|
| 138 |
+
# Load chat template if available
|
| 139 |
+
chat_template_path = os.path.join(MODEL_PATH, "chat_template.jinja")
|
| 140 |
+
if os.path.exists(chat_template_path):
|
| 141 |
+
processor.chat_template = open(chat_template_path).read()
|
| 142 |
+
|
| 143 |
+
model = AutoModelForImageTextToText.from_pretrained(MODEL_PATH, torch_dtype=torch.bfloat16)
|
| 144 |
+
model.to(DEVICE).eval()
|
| 145 |
+
|
| 146 |
+
# Prepare input messages
|
| 147 |
+
messages = [
|
| 148 |
+
{
|
| 149 |
+
"role": "user",
|
| 150 |
+
"content": [
|
| 151 |
+
{"type": "image", "image": "./figures/example.jpg"},
|
| 152 |
+
{"type": "text", "text": "Describe the image in detail."},
|
| 153 |
+
],
|
| 154 |
+
}
|
| 155 |
+
]
|
| 156 |
+
|
| 157 |
+
# Process and generate
|
| 158 |
+
inputs = processor.apply_chat_template(
|
| 159 |
+
messages,
|
| 160 |
+
tokenize=True,
|
| 161 |
+
add_generation_prompt=True,
|
| 162 |
+
return_dict=True,
|
| 163 |
+
return_tensors="pt",
|
| 164 |
+
enable_thinking=THINKING_MODE,
|
| 165 |
+
).to(model.device)
|
| 166 |
+
|
| 167 |
+
with torch.no_grad():
|
| 168 |
+
generated_ids = model.generate(
|
| 169 |
+
**inputs,
|
| 170 |
+
max_new_tokens=32768,
|
| 171 |
+
use_cache=True,
|
| 172 |
+
temperature=TEMPERATURE,
|
| 173 |
+
do_sample=TEMPERATURE > 0,
|
| 174 |
+
)
|
| 175 |
+
|
| 176 |
+
output_ids = [out[len(inp):] for inp, out in zip(inputs.input_ids, generated_ids)]
|
| 177 |
+
print(processor.batch_decode(output_ids, skip_special_tokens=True)[0])
|
| 178 |
+
```
|
| 179 |
+
|
| 180 |
+
### Batch Inference
|
| 181 |
+
|
| 182 |
+
```python
|
| 183 |
+
import os
|
| 184 |
+
import torch
|
| 185 |
+
from transformers import AutoModelForImageTextToText, AutoProcessor
|
| 186 |
+
|
| 187 |
+
# Load model & processor
|
| 188 |
+
MODEL_PATH = "tencent/HY-Embodied-0.5"
|
| 189 |
+
DEVICE = "cuda"
|
| 190 |
+
THINKING_MODE = False
|
| 191 |
+
TEMPERATURE = 0.8
|
| 192 |
+
|
| 193 |
+
processor = AutoProcessor.from_pretrained(MODEL_PATH)
|
| 194 |
+
|
| 195 |
+
# Load chat template if available
|
| 196 |
+
chat_template_path = os.path.join(MODEL_PATH, "chat_template.jinja")
|
| 197 |
+
if os.path.exists(chat_template_path):
|
| 198 |
+
processor.chat_template = open(chat_template_path).read()
|
| 199 |
+
|
| 200 |
+
model = AutoModelForImageTextToText.from_pretrained(MODEL_PATH, torch_dtype=torch.bfloat16)
|
| 201 |
+
model.to(DEVICE).eval()
|
| 202 |
+
|
| 203 |
+
# Batch Inference (multiple prompts at once)
|
| 204 |
+
messages_batch = [
|
| 205 |
+
# Sample A: image + text
|
| 206 |
+
[
|
| 207 |
+
{
|
| 208 |
+
"role": "user",
|
| 209 |
+
"content": [
|
| 210 |
+
{"type": "image", "image": "./figures/example.jpg"},
|
| 211 |
+
{"type": "text", "text": "Describe the image in detail."},
|
| 212 |
+
],
|
| 213 |
+
}
|
| 214 |
+
],
|
| 215 |
+
# Sample B: text only
|
| 216 |
+
[
|
| 217 |
+
{
|
| 218 |
+
"role": "user",
|
| 219 |
+
"content": [
|
| 220 |
+
{"type": "text", "text": "How to open a fridge?"},
|
| 221 |
+
],
|
| 222 |
+
}
|
| 223 |
+
],
|
| 224 |
+
]
|
| 225 |
+
|
| 226 |
+
# Process each message independently
|
| 227 |
+
all_inputs = []
|
| 228 |
+
for msgs in messages_batch:
|
| 229 |
+
inp = processor.apply_chat_template(
|
| 230 |
+
msgs,
|
| 231 |
+
tokenize=True,
|
| 232 |
+
add_generation_prompt=True,
|
| 233 |
+
return_dict=True,
|
| 234 |
+
return_tensors="pt",
|
| 235 |
+
enable_thinking=THINKING_MODE,
|
| 236 |
+
)
|
| 237 |
+
all_inputs.append(inp)
|
| 238 |
+
|
| 239 |
+
# Left-pad and batch
|
| 240 |
+
batch = processor.pad(all_inputs, padding=True, padding_side="left").to(model.device)
|
| 241 |
+
|
| 242 |
+
with torch.no_grad():
|
| 243 |
+
batch_generated_ids = model.generate(
|
| 244 |
+
**batch,
|
| 245 |
+
max_new_tokens=32768,
|
| 246 |
+
use_cache=True,
|
| 247 |
+
temperature=TEMPERATURE,
|
| 248 |
+
do_sample=TEMPERATURE > 0,
|
| 249 |
+
)
|
| 250 |
+
|
| 251 |
+
# Decode: strip the padded input portion
|
| 252 |
+
padded_input_len = batch["input_ids"].shape[1]
|
| 253 |
+
for i, msgs in enumerate(messages_batch):
|
| 254 |
+
out_ids = batch_generated_ids[i][padded_input_len:]
|
| 255 |
+
print(f"\n--- Sample {i} ---")
|
| 256 |
+
print(processor.decode(out_ids, skip_special_tokens=True))
|
| 257 |
+
```
|
| 258 |
+
|
| 259 |
+
## 📊 Evaluation
|
| 260 |
+
|
| 261 |
+
### Visual Perception
|
| 262 |
+
|
| 263 |
+
> **Note**: We evaluated HY-Embodied-0.5 MoT-2B across 22 embodied-relevant benchmarks against models of similar size. For detailed performance metrics and methodology, please refer to our technical report.
|
| 264 |
+
|
| 265 |
+
> **Note**: We observed that small models from the Qwen3.5 series produce repetitive thinking patterns in some benchmarks, which leads to lower overall results. Therefore, we compare against Qwen3-VL models in our evaluations.
|
| 266 |
+
|
| 267 |
+
| Benchmark | HY-Embodied 0.5 MoT-2B | Qwen3-VL 2B | Qwen3-VL 4B | RoboBrain 2.5 4B | MiMo-Embodied 7B |
|
| 268 |
+
|-----------|------------------------|-------------|-------------|------------------|------------------|
|
| 269 |
+
| CV-Bench | **89.2** | 80.0 | 85.7 | 86.9 | 88.8 |
|
| 270 |
+
| DA-2K | **92.3** | 69.5 | 76.5 | 79.4 | 72.2 |
|
| 271 |
+
|
| 272 |
+
### Embodied Understanding
|
| 273 |
+
|
| 274 |
+
| Benchmark | HY-Embodied 0.5 MoT-2B | Qwen3-VL 2B | Qwen3-VL 4B | RoboBrain 2.5 4B | MiMo-Embodied 7B |
|
| 275 |
+
|-----------|------------------------|-------------|-------------|------------------|------------------|
|
| 276 |
+
| ERQA | **54.5** | 41.8 | 47.3 | 43.3 | 46.8 |
|
| 277 |
+
| EmbSpatial-Bench | **82.8** | 75.9 | 80.7 | 73.8 | 76.2 |
|
| 278 |
+
| RoboBench-MCQ | **49.2** | 36.9 | 45.8 | 44.4 | 43.6 |
|
| 279 |
+
| RoboBench-Planning | 54.2 | 36.2 | 36.4 | 39.2 | **58.7** |
|
| 280 |
+
| RoboSpatial-Home | 55.7 | 45.3 | **63.2** | 62.3 | 61.8 |
|
| 281 |
+
| ShareRobot-Aff. | **26.8** | 19.8 | 25.5 | 25.5 | 9.0 |
|
| 282 |
+
| ShareRobot-Traj. | 73.3 | 41.6 | 62.2 | **81.4** | 50.6 |
|
| 283 |
+
| Ego-Plan2 | 45.5 | 35.5 | 38.8 | **52.6** | 39.9 |
|
| 284 |
+
|
| 285 |
+
### Spatial Understanding
|
| 286 |
+
|
| 287 |
+
| Benchmark | HY-Embodied 0.5 MoT-2B | Qwen3-VL 2B | Qwen3-VL 4B | RoboBrain 2.5 4B | MiMo-Embodied 7B |
|
| 288 |
+
|-----------|------------------------|-------------|-------------|------------------|------------------|
|
| 289 |
+
| 3DSRBench | **57.0** | 39.9 | 43.9 | 44.8 | 42.0 |
|
| 290 |
+
| All-Angles Bench | **55.1** | 42.3 | 46.7 | 43.8 | 49.0 |
|
| 291 |
+
| MindCube | **66.3** | 28.4 | 31.0 | 26.9 | 36.2 |
|
| 292 |
+
| MMSI-Bench | **33.2** | 23.6 | 25.1 | 20.5 | 31.9 |
|
| 293 |
+
| RefSpatial-Bench | 45.8 | 28.9 | 45.3 | **56.0** | 48.0 |
|
| 294 |
+
| SAT | 76.7 | 45.3 | 56.7 | 51.3 | **78.7** |
|
| 295 |
+
| SIBench-mini | **58.2** | 42.0 | 50.9 | 47.3 | 53.1 |
|
| 296 |
+
| SITE-Bench-Image | **62.7** | 52.3 | 61.0 | 57.9 | 49.9 |
|
| 297 |
+
| SITE-Bench-Video | **63.5** | 52.2 | 58.0 | 54.8 | 58.9 |
|
| 298 |
+
| ViewSpatial | **53.1** | 37.2 | 41.6 | 36.6 | 36.1 |
|
| 299 |
+
| VSIBench | **60.5** | 48.0 | 55.2 | 41.7 | 48.5 |
|
| 300 |
+
| Where2Place | **68.0** | 45.0 | 59.0 | 65.0 | 63.6 |
|
| 301 |
+
|
| 302 |
+
*Note: Results for HY-Embodied-0.5 MoT-2B are reported in thinking mode, while for all other models, we report the better performance between non-thinking and thinking modes.*
|
| 303 |
+
|
| 304 |
+
## 📚 Citation
|
| 305 |
+
If you find it useful for your research and applications, please cite our paper using this BibTeX:
|
| 306 |
+
```bibtex
|
| 307 |
+
@article{tencent2026hyembodied05,
|
| 308 |
+
title={HY-Embodied-0.5: Embodied Foundation Models for Real-World Agents},
|
| 309 |
+
author={Tencent Robotics X and HY Vision Team},
|
| 310 |
+
journal={arXiv preprint arXiv:2604.07430},
|
| 311 |
+
year={2026}
|
| 312 |
+
}
|
| 313 |
+
```
|
| 314 |
+
|
| 315 |
+
## 🙏 Acknowledgements
|
| 316 |
+
|
| 317 |
+
We thank the Hugging Face community for their support and the open-source contributions that made this implementation possible.
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"crop_size": null,
|
| 3 |
+
"data_format": "channels_first",
|
| 4 |
+
"default_to_square": true,
|
| 5 |
+
"device": null,
|
| 6 |
+
"disable_grouping": null,
|
| 7 |
+
"do_center_crop": null,
|
| 8 |
+
"do_convert_rgb": true,
|
| 9 |
+
"do_normalize": true,
|
| 10 |
+
"do_pad": null,
|
| 11 |
+
"do_rescale": true,
|
| 12 |
+
"do_resize": true,
|
| 13 |
+
"image_mean": [
|
| 14 |
+
0.5,
|
| 15 |
+
0.5,
|
| 16 |
+
0.5
|
| 17 |
+
],
|
| 18 |
+
"image_processor_type": "Qwen2VLImageProcessorFast",
|
| 19 |
+
"image_std": [
|
| 20 |
+
0.5,
|
| 21 |
+
0.5,
|
| 22 |
+
0.5
|
| 23 |
+
],
|
| 24 |
+
"input_data_format": null,
|
| 25 |
+
"max_pixels": 4194304,
|
| 26 |
+
"merge_size": 2,
|
| 27 |
+
"min_pixels": 25088,
|
| 28 |
+
"pad_size": null,
|
| 29 |
+
"patch_size": 16,
|
| 30 |
+
"processor_class": "HunYuanVLMoTProcessor",
|
| 31 |
+
"resample": 3,
|
| 32 |
+
"rescale_factor": 0.00392156862745098,
|
| 33 |
+
"return_tensors": null,
|
| 34 |
+
"size": {
|
| 35 |
+
"longest_edge": 4194304,
|
| 36 |
+
"shortest_edge": 25088
|
| 37 |
+
},
|
| 38 |
+
"temporal_patch_size": 1
|
| 39 |
+
}
|
blobs/580d310c2d211aca166288a207e972ced9bb0100
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
*.safetensors filter=lfs diff=lfs merge=lfs -text
|
blobs/6c83b16a6412e25a7bae459daf7e93466c885093
ADDED
|
@@ -0,0 +1,78 @@
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|
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|
|
|
|
|
|
|
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|
|
|
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|
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|
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|
|
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|
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|
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|
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|
|
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|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
| 1 |
+
TENCENT HY COMMUNITY LICENSE AGREEMENT
|
| 2 |
+
Tencent HY Embodied Release Date: April 3, 2026
|
| 3 |
+
THIS LICENSE AGREEMENT DOES NOT APPLY IN THE EUROPEAN UNION, UNITED KINGDOM AND SOUTH KOREA AND IS EXPRESSLY LIMITED TO THE TERRITORY, AS DEFINED BELOW.
|
| 4 |
+
By clicking to agree or by using, reproducing, modifying, distributing, performing or displaying any portion or element of the Tencent HY Works, including via any Hosted Service, You will be deemed to have recognized and accepted the content of this Agreement, which is effective immediately.
|
| 5 |
+
1. DEFINITIONS.
|
| 6 |
+
a. “Acceptable Use Policy” shall mean the policy made available by Tencent as set forth in the Exhibit A.
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| 7 |
+
b. “Agreement” shall mean the terms and conditions for use, reproduction, distribution, modification, performance and displaying of Tencent HY Works or any portion or element thereof set forth herein.
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c. “Documentation” shall mean the specifications, manuals and documentation for Tencent HY made publicly available by Tencent.
|
| 9 |
+
d. “Hosted Service” shall mean a hosted service offered via an application programming interface (API), web access, or any other electronic or remote means.
|
| 10 |
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e. “Licensee,” “You” or “Your” shall mean a natural person or legal entity exercising the rights granted by this Agreement and/or using the Tencent HY Works for any purpose and in any field of use.
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| 11 |
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f. “Materials” shall mean, collectively, Tencent’s proprietary Tencent HY and Documentation (and any portion thereof) as made available by Tencent under this Agreement.
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| 12 |
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g. “Model Derivatives” shall mean all: (i) modifications to Tencent HY or any Model Derivative of Tencent HY; (ii) works based on Tencent HY or any Model Derivative of Tencent HY; or (iii) any other machine learning model which is created by transfer of patterns of the weights, parameters, operations, or Output of Tencent HY or any Model Derivative of Tencent HY, to that model in order to cause that model to perform similarly to Tencent HY or a Model Derivative of Tencent HY, including distillation methods, methods that use intermediate data representations, or methods based on the generation of synthetic data Outputs by Tencent HY or a Model Derivative of Tencent HY for training that model. For clarity, Outputs by themselves are not deemed Model Derivatives.
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h. “Output” shall mean the information and/or content output of Tencent HY or a Model Derivative that results from operating or otherwise using Tencent HY or a Model Derivative, including via a Hosted Service.
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j. “Tencent HY” shall mean the large language models, text/image/video/audio/3D generation models, and multimodal large language models and their software and algorithms, including trained model weights, parameters (including optimizer states), machine-learning model code, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing made publicly available by Us, including, without limitation to, Tencent HY Embodied released at [https://github.com/Tencent-Hunyuan/HY-Embodied].
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| 46 |
+
a. The term of this Agreement shall commence upon Your acceptance of this Agreement or access to the Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein.
|
| 47 |
+
b. We may terminate this Agreement if You breach any of the terms or conditions of this Agreement. Upon termination of this Agreement, You must promptly delete and cease use of the Tencent HY Works. Sections 6(a), 6(c), 7 and 9 shall survive the termination of this Agreement.
|
| 48 |
+
9. GOVERNING LAW AND JURISDICTION.
|
| 49 |
+
a. This Agreement and any dispute arising out of or relating to it will be governed by the laws of the Hong Kong Special Administrative Region of the People’s Republic of China, without regard to conflict of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement.
|
| 50 |
+
b. Exclusive jurisdiction and venue for any dispute arising out of or relating to this Agreement will be a court of competent jurisdiction in the Hong Kong Special Administrative Region of the People’s Republic of China, and Tencent and Licensee consent to the exclusive jurisdiction of such court with respect to any such dispute.
|
| 51 |
+
|
| 52 |
+
EXHIBIT A
|
| 53 |
+
ACCEPTABLE USE POLICY
|
| 54 |
+
|
| 55 |
+
Tencent reserves the right to update this Acceptable Use Policy from time to time.
|
| 56 |
+
Last modified: December 30, 2025
|
| 57 |
+
|
| 58 |
+
Tencent endeavors to promote safe and fair use of its tools and features, including Tencent HY. You agree not to use Tencent HY or Model Derivatives:
|
| 59 |
+
1. Outside the Territory;
|
| 60 |
+
2. In any way that violates any applicable national, federal, state, local, international or any other law or regulation;
|
| 61 |
+
3. To harm Yourself or others;
|
| 62 |
+
4. To repurpose or distribute output from Tencent HY or any Model Derivatives to harm Yourself or others;
|
| 63 |
+
5. To override or circumvent the safety guardrails and safeguards We have put in place;
|
| 64 |
+
6. For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
|
| 65 |
+
7. To generate or disseminate verifiably false information and/or content with the purpose of harming others or influencing elections;
|
| 66 |
+
8. To generate or facilitate false online engagement, including fake reviews and other means of fake online engagement;
|
| 67 |
+
9. To intentionally defame, disparage or otherwise harass others;
|
| 68 |
+
10. To generate and/or disseminate malware (including ransomware) or any other content to be used for the purpose of harming electronic systems;
|
| 69 |
+
11. To generate or disseminate personal identifiable information with the purpose of harming others;
|
| 70 |
+
12. To generate or disseminate information (including images, code, posts, articles), and place the information in any public context (including –through the use of bot generated tweets), without expressly and conspicuously identifying that the information and/or content is machine generated;
|
| 71 |
+
13. To impersonate another individual without consent, authorization, or legal right;
|
| 72 |
+
14. To make high-stakes automated decisions in domains that affect an individual’s safety, rights or wellbeing (e.g., law enforcement, migration, medicine/health, management of critical infrastructure, safety components of products, essential services, credit, employment, housing, education, social scoring, or insurance);
|
| 73 |
+
15. In a manner that violates or disrespects the social ethics and moral standards of other countries or regions;
|
| 74 |
+
16. To perform, facilitate, threaten, incite, plan, promote or encourage violent extremism or terrorism;
|
| 75 |
+
17. For any use intended to discriminate against or harm individuals or groups based on protected characteristics or categories, online or offline social behavior or known or predicted personal or personality characteristics;
|
| 76 |
+
18. To intentionally exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
|
| 77 |
+
19. For military purposes;
|
| 78 |
+
20. To engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or other professional practices.
|
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| 5 |
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|
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"AutoConfig": "configuration_hunyuan_vl_mot.HunYuanVLMoTConfig",
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"hidden_size": 2048,
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| 33 |
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"mlp_bias": false,
|
| 34 |
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|
| 35 |
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"norm_type": "rms",
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|
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|
| 63 |
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| 64 |
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| 65 |
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|
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|
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|
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