Upload folder using huggingface_hub
Browse files- added_tokens.json +10 -0
- chat_template.jinja +172 -0
- config.json +152 -0
- configuration_minicpm_sala.py +260 -0
- generation_config.json +11 -0
- model-00001-of-00002.safetensors +3 -0
- model-00002-of-00002.safetensors +3 -0
- model.safetensors.index.json +1098 -0
- modeling_minicpm_sala.py +0 -0
- quant_log.csv +233 -0
- quantize_config.json +54 -0
- special_tokens_map.json +46 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +242 -0
added_tokens.json
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{
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"</tool_call>": 73443,
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"<tool_call>": 73442,
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"<|fim_middle|>": 73446,
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"<|fim_prefix|>": 73445,
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"<|fim_suffix|>": 73447,
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"<|im_end|>": 73440,
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"<|im_sep|>": 73444,
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"<|im_start|>": 73441
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}
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chat_template.jinja
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{%- if tools %}
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{%- set tool_definitions %}
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{{- "# Tools\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson(ensure_ascii=False) }}
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{%- endfor %}
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{{- '\n</tools>\n\nTool usage guidelines:\n- You may call zero or more functions. If no function calls are needed, just answer normally and do not include any <function ... </function>.\n- When calling a function, return an XML object within <function ... </function> using:\n<function name="function-name"><param name="param-name">param-value</param></function>\n- param-value may be multi-line. If it contains <, & or newline characters, wrap it in a CDATA block: <param name="param-name"><![CDATA[...multi-line value...]]></param>' }}
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{%- endset %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{%- if '<tool_def_sep>' in messages[0].content %}
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{{- messages[0].content.replace('<tool_def_sep>', tool_definitions) }}
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{%- else %}
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{{- messages[0].content + '\n\n' + tool_definitions }}
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{%- endif %}
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{%- else %}
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{{- tool_definitions.lstrip() }}
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{%- endif %}
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{{- '<|im_end|>\n' }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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| 26 |
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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| 28 |
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{%- for message in messages[::-1] %}
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| 29 |
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{%- set index = (messages|length - 1) - loop.index0 %}
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| 30 |
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{%- 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>')) %}
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| 31 |
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{%- set ns.multi_step_tool = false %}
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| 32 |
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{%- set ns.last_query_index = index %}
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| 33 |
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{%- endif %}
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| 34 |
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{%- endfor %}
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| 35 |
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{%- for message in messages %}
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| 36 |
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{%- if message.content is string %}
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{%- set content = message.content %}
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{%- else %}
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| 39 |
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{%- set content = '' %}
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| 40 |
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{%- endif %}
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| 41 |
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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| 43 |
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{%- elif message.role == "assistant" %}
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| 44 |
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{%- set reasoning_content = '' %}
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| 45 |
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{%- if message.reasoning_content is string %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in content %}
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| 49 |
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{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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| 50 |
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{%- set content = content.split('</think>')[-1].lstrip('\n') %}
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| 51 |
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{%- endif %}
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| 52 |
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{%- endif %}
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| 53 |
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| 54 |
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{%- if message.tool_calls %}
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| 55 |
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{%- set content_parts = content.split('<tool_sep>') %}
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| 56 |
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{%- set processed_content = content_parts[0] %}
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| 57 |
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{%- set tool_calls_count = message.tool_calls|length %}
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| 58 |
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{%- set tool_sep_count = content_parts|length - 1 %}
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| 59 |
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{%- set min_count = [tool_calls_count, tool_sep_count]|min %}
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| 60 |
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| 61 |
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{%- for i in range(1, content_parts|length) %}
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| 62 |
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{%- set tool_index = i - 1 %}
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| 63 |
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{%- if tool_index < tool_calls_count %}
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| 64 |
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{%- set tool_call = message.tool_calls[tool_index] %}
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| 65 |
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{%- if tool_call.function %}
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| 66 |
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{%- set tool_call = tool_call.function %}
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| 67 |
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{%- endif %}
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{%- set single_tool_xml %}
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{{- '<function name="' ~ tool_call.name ~ '">' }}
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| 70 |
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{%- if tool_call.arguments %}
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| 71 |
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{%- set args_dict = tool_call.arguments %}
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| 72 |
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{%- for param_name, param_value in args_dict.items() %}
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| 73 |
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{{- '<param name="' ~ param_name ~ '">' }}
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| 74 |
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{%- if param_value is string and ('<' in param_value or '&' in param_value or '\n' in param_value) %}
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| 75 |
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{{- '<![CDATA[' + param_value + ']]>' }}
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| 76 |
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{%- else %}
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| 77 |
+
{{- param_value }}
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| 78 |
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{%- endif %}
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| 79 |
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{{- '</param>' }}
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| 80 |
+
{%- endfor %}
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| 81 |
+
{%- endif %}
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| 82 |
+
{{- '</function>' }}
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| 83 |
+
{%- endset %}
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| 84 |
+
{%- set processed_content = processed_content + single_tool_xml + content_parts[i] %}
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| 85 |
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{%- else %}
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| 86 |
+
{%- set processed_content = processed_content + content_parts[i] %}
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| 87 |
+
{%- endif %}
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| 88 |
+
{%- endfor %}
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| 89 |
+
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| 90 |
+
{%- if tool_calls_count > tool_sep_count %}
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| 91 |
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{%- for remaining_index in range(tool_sep_count, tool_calls_count) %}
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| 92 |
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{%- set tool_call = message.tool_calls[remaining_index] %}
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| 93 |
+
{%- if tool_call.function %}
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| 94 |
+
{%- set tool_call = tool_call.function %}
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| 95 |
+
{%- endif %}
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| 96 |
+
{%- set remaining_tool_xml %}
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| 97 |
+
{{- '<function name="' ~ tool_call.name ~ '">' }}
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| 98 |
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{%- if tool_call.arguments %}
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| 99 |
+
{%- set args_dict = tool_call.arguments %}
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| 100 |
+
{%- for param_name, param_value in args_dict.items() %}
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| 101 |
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{{- '<param name="' ~ param_name ~ '">' }}
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| 102 |
+
{%- if param_value is string and ('<' in param_value or '&' in param_value or '\n' in param_value) %}
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| 103 |
+
{{- '<![CDATA[' + param_value + ']]>' }}
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| 104 |
+
{%- else %}
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| 105 |
+
{{- param_value }}
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| 106 |
+
{%- endif %}
|
| 107 |
+
{{- '</param>' }}
|
| 108 |
+
{%- endfor %}
|
| 109 |
+
{%- endif %}
|
| 110 |
+
{{- '</function>' }}
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| 111 |
+
{%- endset %}
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| 112 |
+
{%- set processed_content = processed_content + remaining_tool_xml %}
|
| 113 |
+
{%- endfor %}
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| 114 |
+
{%- endif %}
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| 115 |
+
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| 116 |
+
{%- set content = processed_content %}
|
| 117 |
+
{%- endif %}
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| 118 |
+
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| 119 |
+
{%- if loop.index0 > ns.last_query_index %}
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| 120 |
+
{%- if reasoning_content %}
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| 121 |
+
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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| 122 |
+
{%- else %}
|
| 123 |
+
{{- '<|im_start|>' + message.role + '\n' + content }}
|
| 124 |
+
{%- endif %}
|
| 125 |
+
{%- else %}
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| 126 |
+
{{- '<|im_start|>' + message.role + '\n' + content }}
|
| 127 |
+
{%- endif %}
|
| 128 |
+
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| 129 |
+
{%- if message.tool_calls and not has_tool_sep %}
|
| 130 |
+
{%- for tool_call in message.tool_calls %}
|
| 131 |
+
{%- if (loop.first and content) or (not loop.first) %}
|
| 132 |
+
{{- '\n' }}
|
| 133 |
+
{%- endif %}
|
| 134 |
+
{%- if tool_call.function %}
|
| 135 |
+
{%- set tool_call = tool_call.function %}
|
| 136 |
+
{%- endif %}
|
| 137 |
+
{{- '<function name="' ~ tool_call.name ~ '">' }}
|
| 138 |
+
{%- if tool_call.arguments %}
|
| 139 |
+
{%- set args_dict = tool_call.arguments %}
|
| 140 |
+
{%- for param_name, param_value in args_dict.items() %}
|
| 141 |
+
{{- '<param name="' ~ param_name ~ '">' }}
|
| 142 |
+
{%- if param_value is string and ('<' in param_value or '&' in param_value or '\n' in param_value) %}
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| 143 |
+
{{- '<![CDATA[' + param_value + ']]>' }}
|
| 144 |
+
{%- else %}
|
| 145 |
+
{{- param_value }}
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| 146 |
+
{%- endif %}
|
| 147 |
+
{{- '</param>' }}
|
| 148 |
+
{%- endfor %}
|
| 149 |
+
{%- endif %}
|
| 150 |
+
{{- '</function>' }}
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| 151 |
+
{%- endfor %}
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| 152 |
+
{%- endif %}
|
| 153 |
+
{{- '<|im_end|>\n' }}
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| 154 |
+
{%- elif message.role == "tool" %}
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| 155 |
+
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
| 156 |
+
{{- '<|im_start|>user' }}
|
| 157 |
+
{%- endif %}
|
| 158 |
+
{{- '\n<tool_response>\n' }}
|
| 159 |
+
{%- if message.content is string %}
|
| 160 |
+
{{- content }}
|
| 161 |
+
{%- else %}
|
| 162 |
+
{{- message.content | tojson(ensure_ascii=False) }}
|
| 163 |
+
{%- endif %}
|
| 164 |
+
{{- '\n</tool_response>' }}
|
| 165 |
+
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
| 166 |
+
{{- '<|im_end|>\n' }}
|
| 167 |
+
{%- endif %}
|
| 168 |
+
{%- endif %}
|
| 169 |
+
{%- endfor %}
|
| 170 |
+
{%- if add_generation_prompt %}
|
| 171 |
+
{{- '<|im_start|>assistant\n' }}
|
| 172 |
+
{%- endif %}
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config.json
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|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"MiniCPMSALAForCausalLM"
|
| 4 |
+
],
|
| 5 |
+
"attention_bias": false,
|
| 6 |
+
"attention_dropout": 0.0,
|
| 7 |
+
"attn_use_output_gate": true,
|
| 8 |
+
"attn_use_rope": false,
|
| 9 |
+
"auto_map": {
|
| 10 |
+
"AutoConfig": "configuration_minicpm_sala.MiniCPMSALAConfig",
|
| 11 |
+
"AutoModel": "modeling_minicpm_sala.MiniCPMSALAModel",
|
| 12 |
+
"AutoModelForCausalLM": "modeling_minicpm_sala.MiniCPMSALAForCausalLM",
|
| 13 |
+
"AutoModelForSeq2SeqLM": "modeling_minicpm_sala.MiniCPMSALAForCausalLM",
|
| 14 |
+
"AutoModelForSequenceClassification": "modeling_minicpm_sala.MiniCPMSALAForSequenceClassification"
|
| 15 |
+
},
|
| 16 |
+
"bos_token_id": 1,
|
| 17 |
+
"dim_model_base": 256,
|
| 18 |
+
"dtype": "bfloat16",
|
| 19 |
+
"eos_token_id": [
|
| 20 |
+
2,
|
| 21 |
+
73440
|
| 22 |
+
],
|
| 23 |
+
"head_dim": 128,
|
| 24 |
+
"hidden_act": "silu",
|
| 25 |
+
"hidden_size": 4096,
|
| 26 |
+
"initializer_range": 0.1,
|
| 27 |
+
"intermediate_size": 16384,
|
| 28 |
+
"lightning_head_dim": 128,
|
| 29 |
+
"lightning_nh": 32,
|
| 30 |
+
"lightning_nkv": 32,
|
| 31 |
+
"lightning_scale": "1/sqrt(d)",
|
| 32 |
+
"lightning_use_rope": true,
|
| 33 |
+
"max_position_embeddings": 524288,
|
| 34 |
+
"mixer_types": [
|
| 35 |
+
"minicpm4",
|
| 36 |
+
"lightning-attn",
|
| 37 |
+
"lightning-attn",
|
| 38 |
+
"lightning-attn",
|
| 39 |
+
"lightning-attn",
|
| 40 |
+
"lightning-attn",
|
| 41 |
+
"lightning-attn",
|
| 42 |
+
"lightning-attn",
|
| 43 |
+
"lightning-attn",
|
| 44 |
+
"minicpm4",
|
| 45 |
+
"lightning-attn",
|
| 46 |
+
"lightning-attn",
|
| 47 |
+
"lightning-attn",
|
| 48 |
+
"lightning-attn",
|
| 49 |
+
"lightning-attn",
|
| 50 |
+
"lightning-attn",
|
| 51 |
+
"minicpm4",
|
| 52 |
+
"minicpm4",
|
| 53 |
+
"lightning-attn",
|
| 54 |
+
"lightning-attn",
|
| 55 |
+
"lightning-attn",
|
| 56 |
+
"lightning-attn",
|
| 57 |
+
"minicpm4",
|
| 58 |
+
"lightning-attn",
|
| 59 |
+
"lightning-attn",
|
| 60 |
+
"lightning-attn",
|
| 61 |
+
"lightning-attn",
|
| 62 |
+
"lightning-attn",
|
| 63 |
+
"lightning-attn",
|
| 64 |
+
"minicpm4",
|
| 65 |
+
"minicpm4",
|
| 66 |
+
"minicpm4"
|
| 67 |
+
],
|
| 68 |
+
"model_type": "minicpm_sala",
|
| 69 |
+
"mup_denominator": 32,
|
| 70 |
+
"num_attention_heads": 32,
|
| 71 |
+
"num_hidden_layers": 32,
|
| 72 |
+
"num_key_value_heads": 2,
|
| 73 |
+
"pad_token_id": 2,
|
| 74 |
+
"pretraining_tp": 1,
|
| 75 |
+
"qk_norm": true,
|
| 76 |
+
"quantization_config": {
|
| 77 |
+
"bits": 4,
|
| 78 |
+
"checkpoint_format": "gptq",
|
| 79 |
+
"desc_act": false,
|
| 80 |
+
"dynamic": {
|
| 81 |
+
"+:model\\.model\\.layers\\.0\\..*": {
|
| 82 |
+
"bits": 8
|
| 83 |
+
},
|
| 84 |
+
"+:model\\.model\\.layers\\.31\\..*": {
|
| 85 |
+
"bits": 8
|
| 86 |
+
}
|
| 87 |
+
},
|
| 88 |
+
"format": "gptq",
|
| 89 |
+
"group_size": 128,
|
| 90 |
+
"lm_head": false,
|
| 91 |
+
"meta": {
|
| 92 |
+
"act_group_aware": true,
|
| 93 |
+
"auto_forward_data_parallel": true,
|
| 94 |
+
"damp_auto_increment": 0.01,
|
| 95 |
+
"damp_percent": 0.05,
|
| 96 |
+
"failsafe": {
|
| 97 |
+
"smooth": {
|
| 98 |
+
"group_size_threshold": 128,
|
| 99 |
+
"k": 2.75,
|
| 100 |
+
"type": "mad"
|
| 101 |
+
},
|
| 102 |
+
"strategy": "rtn",
|
| 103 |
+
"threshold": "0.5%"
|
| 104 |
+
},
|
| 105 |
+
"gc_mode": "interval",
|
| 106 |
+
"gptaq": null,
|
| 107 |
+
"hessian": {
|
| 108 |
+
"chunk_bytes": null,
|
| 109 |
+
"chunk_size": null,
|
| 110 |
+
"staging_dtype": "float32"
|
| 111 |
+
},
|
| 112 |
+
"mock_quantization": false,
|
| 113 |
+
"mse": 0.0,
|
| 114 |
+
"offload_to_disk": true,
|
| 115 |
+
"offload_to_disk_path": "./gptqmodel_offload/hqdpgrum-rkaakpxx/",
|
| 116 |
+
"pack_impl": "cpu",
|
| 117 |
+
"quantizer": [
|
| 118 |
+
"gptqmodel:5.7.0"
|
| 119 |
+
],
|
| 120 |
+
"static_groups": false,
|
| 121 |
+
"true_sequential": true,
|
| 122 |
+
"uri": "https://github.com/modelcloud/gptqmodel",
|
| 123 |
+
"vram_strategy": "exclusive",
|
| 124 |
+
"wait_for_submodule_finalizers": false
|
| 125 |
+
},
|
| 126 |
+
"pack_dtype": "int32",
|
| 127 |
+
"quant_method": "gptq",
|
| 128 |
+
"sym": true
|
| 129 |
+
},
|
| 130 |
+
"rms_norm_eps": 1e-06,
|
| 131 |
+
"rope_scaling": null,
|
| 132 |
+
"rope_theta": 10000.0,
|
| 133 |
+
"scale_depth": 1.4,
|
| 134 |
+
"scale_emb": 12,
|
| 135 |
+
"shift_labels": true,
|
| 136 |
+
"sparse_config": {
|
| 137 |
+
"block_size": 64,
|
| 138 |
+
"dense_len": 8192,
|
| 139 |
+
"init_blocks": 1,
|
| 140 |
+
"kernel_size": 32,
|
| 141 |
+
"kernel_stride": 16,
|
| 142 |
+
"topk": 64,
|
| 143 |
+
"use_nope": false,
|
| 144 |
+
"window_size": 2048
|
| 145 |
+
},
|
| 146 |
+
"tie_word_embeddings": false,
|
| 147 |
+
"transformers_version": "4.57.1",
|
| 148 |
+
"use_cache": true,
|
| 149 |
+
"use_output_gate": true,
|
| 150 |
+
"use_output_norm": true,
|
| 151 |
+
"vocab_size": 73448
|
| 152 |
+
}
|
configuration_minicpm_sala.py
ADDED
|
@@ -0,0 +1,260 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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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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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# coding=utf-8
|
| 2 |
+
# Copyright 2025 The OpenBMB Team. All rights reserved.
|
| 3 |
+
#
|
| 4 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 5 |
+
# you may not use this file except in compliance with the License.
|
| 6 |
+
# You may obtain a copy of the License at
|
| 7 |
+
#
|
| 8 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
| 9 |
+
#
|
| 10 |
+
# Unless required by applicable law or agreed to in writing, software
|
| 11 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 12 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 13 |
+
# See the License for the specific language governing permissions and
|
| 14 |
+
# limitations under the License.
|
| 15 |
+
"""MiniCPMSALA model configuration"""
|
| 16 |
+
|
| 17 |
+
from typing import List, Optional
|
| 18 |
+
from transformers.configuration_utils import PretrainedConfig
|
| 19 |
+
from transformers.utils import logging
|
| 20 |
+
|
| 21 |
+
logger = logging.get_logger(__name__)
|
| 22 |
+
|
| 23 |
+
MINICPMSALA_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
|
| 24 |
+
|
| 25 |
+
|
| 26 |
+
class MiniCPMSALAConfig(PretrainedConfig):
|
| 27 |
+
r"""
|
| 28 |
+
This is the configuration class to store the configuration of a [`MiniCPMSALAModel`]. It is used to instantiate an MiniCPMSALA
|
| 29 |
+
model according to the specified arguments, defining the model architecture.
|
| 30 |
+
|
| 31 |
+
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
| 32 |
+
documentation from [`PretrainedConfig`] for more information.
|
| 33 |
+
|
| 34 |
+
|
| 35 |
+
Args:
|
| 36 |
+
vocab_size (`int`, *optional*, defaults to 32000):
|
| 37 |
+
Vocabulary size of the MiniCPMSALA model. Defines the number of different tokens that can be represented by the
|
| 38 |
+
`inputs_ids` passed when calling [`MiniCPMSALAModel`]
|
| 39 |
+
hidden_size (`int`, *optional*, defaults to 4096):
|
| 40 |
+
Dimension of the hidden representations.
|
| 41 |
+
intermediate_size (`int`, *optional*, defaults to 11008):
|
| 42 |
+
Dimension of the MLP representations.
|
| 43 |
+
num_hidden_layers (`int`, *optional*, defaults to 32):
|
| 44 |
+
Number of hidden layers in the Transformer decoder.
|
| 45 |
+
num_attention_heads (`int`, *optional*, defaults to 32):
|
| 46 |
+
Number of attention heads for each attention layer in the Transformer decoder.
|
| 47 |
+
num_key_value_heads (`int`, *optional*):
|
| 48 |
+
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
|
| 49 |
+
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
|
| 50 |
+
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
|
| 51 |
+
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
|
| 52 |
+
by meanpooling all the original heads within that group. For more details checkout [this
|
| 53 |
+
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
|
| 54 |
+
`num_attention_heads`.
|
| 55 |
+
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
|
| 56 |
+
The non-linear activation function (function or string) in the decoder.
|
| 57 |
+
max_position_embeddings (`int`, *optional*, defaults to 2048):
|
| 58 |
+
The maximum sequence length that this model might ever be used with. MiniCPMSALA supports up to 524288 tokens.
|
| 59 |
+
initializer_range (`float`, *optional*, defaults to 0.02):
|
| 60 |
+
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
| 61 |
+
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
|
| 62 |
+
The epsilon used by the rms normalization layers.
|
| 63 |
+
use_cache (`bool`, *optional*, defaults to `True`):
|
| 64 |
+
Whether or not the model should return the last key/values attentions (not used by all models). Only
|
| 65 |
+
relevant if `config.is_decoder=True`.
|
| 66 |
+
pad_token_id (`int`, *optional*):
|
| 67 |
+
Padding token id.
|
| 68 |
+
bos_token_id (`int`, *optional*, defaults to 1):
|
| 69 |
+
Beginning of stream token id.
|
| 70 |
+
eos_token_id (`int`, *optional*, defaults to 2):
|
| 71 |
+
End of stream token id.
|
| 72 |
+
pretraining_tp (`int`, *optional*, defaults to 1):
|
| 73 |
+
Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
|
| 74 |
+
document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
|
| 75 |
+
necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
|
| 76 |
+
issue](https://github.com/pytorch/pytorch/issues/76232).
|
| 77 |
+
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
|
| 78 |
+
Whether to tie weight embeddings
|
| 79 |
+
rope_theta (`float`, *optional*, defaults to 10000.0):
|
| 80 |
+
The base period of the RoPE embeddings.
|
| 81 |
+
rope_scaling (`Dict`, *optional*):
|
| 82 |
+
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
|
| 83 |
+
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
|
| 84 |
+
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
|
| 85 |
+
`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
|
| 86 |
+
these scaling strategies behave:
|
| 87 |
+
https://www.reddit.com/r/LocalMiniCPM/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
|
| 88 |
+
experimental feature, subject to breaking API changes in future versions.
|
| 89 |
+
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
|
| 90 |
+
Whether to use a bias in the query, key, value and output projection layers during self-attention.
|
| 91 |
+
attention_dropout (`float`, *optional*, defaults to 0.0):
|
| 92 |
+
The dropout ratio for the attention probabilities.
|
| 93 |
+
|
| 94 |
+
```python
|
| 95 |
+
>>> from transformers import MiniCPMSALAModel, MiniCPMSALAConfig
|
| 96 |
+
|
| 97 |
+
>>> # Initializing a MiniCPMSALA style configuration
|
| 98 |
+
>>> configuration = MiniCPMSALAConfig()
|
| 99 |
+
|
| 100 |
+
>>> # Initializing a model from the minicpm_sala style configuration
|
| 101 |
+
>>> model = MiniCPMSALAModel(configuration)
|
| 102 |
+
|
| 103 |
+
>>> # Accessing the model configuration
|
| 104 |
+
>>> configuration = model.config
|
| 105 |
+
```"""
|
| 106 |
+
|
| 107 |
+
model_type = "minicpm_sala"
|
| 108 |
+
keys_to_ignore_at_inference = ["past_key_values"]
|
| 109 |
+
|
| 110 |
+
def __init__(
|
| 111 |
+
self,
|
| 112 |
+
vocab_size=32000,
|
| 113 |
+
hidden_size=4096,
|
| 114 |
+
intermediate_size=11008,
|
| 115 |
+
num_hidden_layers=32,
|
| 116 |
+
num_attention_heads=32,
|
| 117 |
+
num_key_value_heads=None,
|
| 118 |
+
hidden_act="silu",
|
| 119 |
+
max_position_embeddings=2048,
|
| 120 |
+
initializer_range=0.02,
|
| 121 |
+
rms_norm_eps=1e-6,
|
| 122 |
+
use_cache=True,
|
| 123 |
+
pad_token_id=None,
|
| 124 |
+
bos_token_id=1,
|
| 125 |
+
eos_token_id=2,
|
| 126 |
+
pretraining_tp=1,
|
| 127 |
+
tie_word_embeddings=True,
|
| 128 |
+
rope_theta=10000.0,
|
| 129 |
+
rope_scaling=None,
|
| 130 |
+
attention_bias=False,
|
| 131 |
+
attention_dropout=0.0,
|
| 132 |
+
scale_emb=1,
|
| 133 |
+
dim_model_base=1,
|
| 134 |
+
scale_depth=1,
|
| 135 |
+
mup_denominator=32,
|
| 136 |
+
sparse_config=None,
|
| 137 |
+
mixer_types: List[str] = ["minicpm4"],
|
| 138 |
+
head_dim: Optional[int] = None,
|
| 139 |
+
use_output_gate: bool = False,
|
| 140 |
+
use_output_norm: bool = False,
|
| 141 |
+
lightning_use_rope: bool = True,
|
| 142 |
+
lightning_nkv: Optional[int] = None,
|
| 143 |
+
lightning_nh: Optional[int] = None,
|
| 144 |
+
qk_norm: bool = False,
|
| 145 |
+
lightning_head_dim: Optional[int] = None,
|
| 146 |
+
rand_init: bool = False,
|
| 147 |
+
train_mlp: bool = True,
|
| 148 |
+
attn_use_rope: bool = True,
|
| 149 |
+
lightning_scale: str = "1/sqrt(d)",
|
| 150 |
+
shift_labels: bool = True,
|
| 151 |
+
attn_use_output_gate: bool = False,
|
| 152 |
+
**kwargs,
|
| 153 |
+
):
|
| 154 |
+
|
| 155 |
+
self.vocab_size = vocab_size
|
| 156 |
+
self.max_position_embeddings = max_position_embeddings
|
| 157 |
+
self.hidden_size = hidden_size
|
| 158 |
+
self.intermediate_size = intermediate_size
|
| 159 |
+
self.num_hidden_layers = num_hidden_layers
|
| 160 |
+
self.num_attention_heads = num_attention_heads
|
| 161 |
+
|
| 162 |
+
# for backward compatibility
|
| 163 |
+
if num_key_value_heads is None:
|
| 164 |
+
num_key_value_heads = num_attention_heads
|
| 165 |
+
|
| 166 |
+
self.num_key_value_heads = num_key_value_heads
|
| 167 |
+
self.hidden_act = hidden_act
|
| 168 |
+
self.initializer_range = initializer_range
|
| 169 |
+
self.rms_norm_eps = rms_norm_eps
|
| 170 |
+
self.pretraining_tp = pretraining_tp
|
| 171 |
+
self.use_cache = use_cache
|
| 172 |
+
self.rope_theta = rope_theta
|
| 173 |
+
self.rope_scaling = rope_scaling
|
| 174 |
+
self.attention_bias = attention_bias
|
| 175 |
+
self.attention_dropout = attention_dropout
|
| 176 |
+
self.scale_emb = scale_emb
|
| 177 |
+
self.dim_model_base = dim_model_base
|
| 178 |
+
self.scale_depth = scale_depth
|
| 179 |
+
# only used for Eagle Head
|
| 180 |
+
self.mup_denominator = mup_denominator
|
| 181 |
+
|
| 182 |
+
# sparse config
|
| 183 |
+
self.sparse_config = sparse_config
|
| 184 |
+
|
| 185 |
+
self.mixer_types = mixer_types
|
| 186 |
+
if self.mixer_types is None or len(self.mixer_types) == 0:
|
| 187 |
+
# Default to MiniCPMSALA4 (full attention) for all layers
|
| 188 |
+
self.mixer_types = ["minicpm4"] * self.num_hidden_layers
|
| 189 |
+
elif len(self.mixer_types) < self.num_hidden_layers:
|
| 190 |
+
self.mixer_types = (mixer_types * self.num_hidden_layers)[
|
| 191 |
+
: self.num_hidden_layers
|
| 192 |
+
]
|
| 193 |
+
elif len(self.mixer_types) == self.num_hidden_layers:
|
| 194 |
+
self.mixer_types = mixer_types
|
| 195 |
+
else:
|
| 196 |
+
raise ValueError(f"Invalid number of mixer types: {len(self.mixer_types)}")
|
| 197 |
+
assert len(self.mixer_types) == self.num_hidden_layers
|
| 198 |
+
|
| 199 |
+
# for Lightning
|
| 200 |
+
if head_dim is None:
|
| 201 |
+
head_dim = self.hidden_size // self.num_attention_heads
|
| 202 |
+
self.head_dim = head_dim
|
| 203 |
+
self.use_output_norm = use_output_norm
|
| 204 |
+
self.use_output_gate = use_output_gate
|
| 205 |
+
self.lightning_use_rope = lightning_use_rope
|
| 206 |
+
self.qk_norm = qk_norm
|
| 207 |
+
self.lightning_nkv = (
|
| 208 |
+
lightning_nkv if lightning_nkv is not None else self.num_key_value_heads
|
| 209 |
+
)
|
| 210 |
+
self.lightning_nh = (
|
| 211 |
+
lightning_nh if lightning_nh is not None else self.num_attention_heads
|
| 212 |
+
)
|
| 213 |
+
self.lightning_head_dim = (
|
| 214 |
+
lightning_head_dim if lightning_head_dim is not None else self.head_dim
|
| 215 |
+
)
|
| 216 |
+
self.lightning_scale = lightning_scale
|
| 217 |
+
self.attn_use_rope = attn_use_rope
|
| 218 |
+
self.shift_labels = shift_labels
|
| 219 |
+
self.attn_use_output_gate = attn_use_output_gate
|
| 220 |
+
|
| 221 |
+
super().__init__(
|
| 222 |
+
pad_token_id=pad_token_id,
|
| 223 |
+
bos_token_id=bos_token_id,
|
| 224 |
+
eos_token_id=eos_token_id,
|
| 225 |
+
tie_word_embeddings=tie_word_embeddings,
|
| 226 |
+
**kwargs,
|
| 227 |
+
)
|
| 228 |
+
try:
|
| 229 |
+
import flash_attn
|
| 230 |
+
|
| 231 |
+
self._attn_implementation = "flash_attention_2"
|
| 232 |
+
except ImportError:
|
| 233 |
+
pass
|
| 234 |
+
|
| 235 |
+
def _rope_scaling_validation(self):
|
| 236 |
+
"""
|
| 237 |
+
Validate the `rope_scaling` configuration.
|
| 238 |
+
"""
|
| 239 |
+
if self.rope_scaling is None:
|
| 240 |
+
return
|
| 241 |
+
|
| 242 |
+
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
|
| 243 |
+
raise ValueError(
|
| 244 |
+
"`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
|
| 245 |
+
f"got {self.rope_scaling}"
|
| 246 |
+
)
|
| 247 |
+
rope_scaling_type = self.rope_scaling.get("type", None)
|
| 248 |
+
rope_scaling_factor = self.rope_scaling.get("factor", None)
|
| 249 |
+
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
|
| 250 |
+
raise ValueError(
|
| 251 |
+
f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
|
| 252 |
+
)
|
| 253 |
+
if (
|
| 254 |
+
rope_scaling_factor is None
|
| 255 |
+
or not isinstance(rope_scaling_factor, float)
|
| 256 |
+
or rope_scaling_factor <= 1.0
|
| 257 |
+
):
|
| 258 |
+
raise ValueError(
|
| 259 |
+
f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}"
|
| 260 |
+
)
|
generation_config.json
ADDED
|
@@ -0,0 +1,11 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_from_model_config": true,
|
| 3 |
+
"bos_token_id": 1,
|
| 4 |
+
"do_sample": true,
|
| 5 |
+
"eos_token_id": [
|
| 6 |
+
2,
|
| 7 |
+
73440
|
| 8 |
+
],
|
| 9 |
+
"pad_token_id": 2,
|
| 10 |
+
"transformers_version": "4.57.1"
|
| 11 |
+
}
|
model-00001-of-00002.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:ca3de35351b80d007c21dc2a9236f503a74b8ce7c4043afaa997bc2ab021119e
|
| 3 |
+
size 4291529723
|
model-00002-of-00002.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:aee4f2b8308307f06b235c79ef3607f37f6a48441587488dba2157001be4fbbc
|
| 3 |
+
size 2125112105
|
model.safetensors.index.json
ADDED
|
@@ -0,0 +1,1098 @@
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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quant_log.csv
ADDED
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| 1 |
+
layer,module,loss,samples,damp,time
|
| 2 |
+
0,mlp.gate_proj,0.0000019575,0.05000,2.282
|
| 3 |
+
0,mlp.up_proj,0.0000027544,0.05000,2.295
|
| 4 |
+
0,mlp.down_proj,0.0000010484,0.05000,3.485
|
| 5 |
+
0,self_attn.o_proj,failsafe(rtn): 0.0018692,0.00000,0.133
|
| 6 |
+
0,self_attn.k_proj,0.0000001276,0.05000,1.896
|
| 7 |
+
0,self_attn.q_proj,0.0000026756,0.05000,1.900
|
| 8 |
+
0,self_attn.v_proj,0.0000002852,0.05000,1.926
|
| 9 |
+
0,self_attn.o_gate,0.0000056989,0.05000,0.416
|
| 10 |
+
1,mlp.up_proj,0.0000019776,0.05000,2.523
|
| 11 |
+
1,mlp.gate_proj,0.0000018764,0.05000,2.584
|
| 12 |
+
1,mlp.down_proj,0.0000005803,0.05000,3.835
|
| 13 |
+
1,self_attn.v_proj,0.0000274517,0.05000,3.357
|
| 14 |
+
1,self_attn.o_proj,0.0000006033,0.05000,3.382
|
| 15 |
+
1,self_attn.q_proj,0.0000212897,0.05000,3.414
|
| 16 |
+
1,self_attn.k_proj,0.0000216100,0.05000,3.420
|
| 17 |
+
2,mlp.gate_proj,0.0000023568,0.05000,2.246
|
| 18 |
+
2,mlp.up_proj,0.0000023672,0.05000,2.256
|
| 19 |
+
2,mlp.down_proj,0.0000010073,0.05000,3.446
|
| 20 |
+
2,self_attn.q_proj,0.0000107203,0.05000,2.823
|
| 21 |
+
2,self_attn.v_proj,0.0000126155,0.05000,2.848
|
| 22 |
+
2,self_attn.o_proj,0.0000002600,0.05000,2.851
|
| 23 |
+
2,self_attn.k_proj,0.0000106647,0.05000,2.854
|
| 24 |
+
3,mlp.up_proj,0.0000042655,0.05000,2.150
|
| 25 |
+
3,mlp.gate_proj,0.0000048281,0.05000,2.155
|
| 26 |
+
3,mlp.down_proj,0.0000014951,0.05000,3.343
|
| 27 |
+
3,self_attn.v_proj,0.0000143808,0.05000,2.827
|
| 28 |
+
3,self_attn.q_proj,0.0000133468,0.05000,2.856
|
| 29 |
+
3,self_attn.o_proj,0.0000003673,0.05000,2.865
|
| 30 |
+
3,self_attn.k_proj,0.0000136954,0.05000,2.868
|
| 31 |
+
4,mlp.up_proj,0.0000057488,0.05000,2.549
|
| 32 |
+
4,mlp.gate_proj,0.0000063297,0.05000,2.552
|
| 33 |
+
4,mlp.down_proj,0.0000016230,0.05000,3.892
|
| 34 |
+
4,self_attn.q_proj,0.0000106056,0.05000,3.481
|
| 35 |
+
4,self_attn.v_proj,0.0000128426,0.05000,3.494
|
| 36 |
+
4,self_attn.k_proj,0.0000108418,0.05000,3.509
|
| 37 |
+
4,self_attn.o_proj,0.0000002972,0.05000,3.519
|
| 38 |
+
5,mlp.up_proj,0.0000071429,0.05000,2.613
|
| 39 |
+
5,mlp.gate_proj,0.0000078490,0.05000,2.635
|
| 40 |
+
5,mlp.down_proj,0.0000017132,0.05000,3.949
|
| 41 |
+
5,self_attn.v_proj,0.0000129791,0.05000,3.126
|
| 42 |
+
5,self_attn.k_proj,0.0000121584,0.05000,3.347
|
| 43 |
+
5,self_attn.q_proj,0.0000115177,0.05000,3.356
|
| 44 |
+
5,self_attn.o_proj,0.0000004817,0.05000,3.377
|
| 45 |
+
6,mlp.up_proj,0.0000088197,0.05000,2.675
|
| 46 |
+
6,mlp.gate_proj,0.0000095654,0.05000,2.748
|
| 47 |
+
6,mlp.down_proj,0.0000019187,0.05000,3.957
|
| 48 |
+
6,self_attn.o_proj,0.0000004831,0.05000,2.738
|
| 49 |
+
6,self_attn.q_proj,0.0000111541,0.05000,2.795
|
| 50 |
+
6,self_attn.k_proj,0.0000133217,0.05000,2.824
|
| 51 |
+
6,self_attn.v_proj,0.0000116766,0.05000,2.834
|
| 52 |
+
7,mlp.up_proj,0.0000092302,0.05000,1.289
|
| 53 |
+
7,mlp.gate_proj,0.0000097952,0.05000,1.293
|
| 54 |
+
7,mlp.down_proj,0.0000022242,0.05000,2.475
|
| 55 |
+
7,self_attn.v_proj,0.0000109299,0.05000,2.075
|
| 56 |
+
7,self_attn.k_proj,0.0000121253,0.05000,2.156
|
| 57 |
+
7,self_attn.q_proj,0.0000099677,0.05000,2.286
|
| 58 |
+
7,self_attn.o_proj,0.0000005160,0.05000,2.289
|
| 59 |
+
8,mlp.gate_proj,0.0000104388,0.05000,1.974
|
| 60 |
+
8,mlp.up_proj,0.0000095090,0.05000,2.055
|
| 61 |
+
8,mlp.down_proj,0.0000021816,0.05000,3.211
|
| 62 |
+
8,self_attn.k_proj,0.0000085051,0.05000,2.614
|
| 63 |
+
8,self_attn.v_proj,0.0000075190,0.05000,2.743
|
| 64 |
+
8,self_attn.o_proj,0.0000008019,0.05000,2.750
|
| 65 |
+
8,self_attn.q_proj,0.0000069249,0.05000,2.772
|
| 66 |
+
9,mlp.gate_proj,0.0000114569,0.05000,1.817
|
| 67 |
+
9,mlp.up_proj,0.0000109181,0.05000,1.822
|
| 68 |
+
9,mlp.down_proj,0.0000028727,0.05000,3.040
|
| 69 |
+
9,self_attn.o_proj,failsafe(rtn): 0.0020905,0.00000,0.095
|
| 70 |
+
9,self_attn.v_proj,0.0000005232,0.05000,1.285
|
| 71 |
+
9,self_attn.k_proj,0.0000008164,0.05000,1.304
|
| 72 |
+
9,self_attn.q_proj,0.0000084581,0.05000,1.328
|
| 73 |
+
9,self_attn.o_gate,0.0000059179,0.05000,0.404
|
| 74 |
+
10,mlp.up_proj,0.0000110046,0.05000,1.756
|
| 75 |
+
10,mlp.gate_proj,0.0000113558,0.05000,1.940
|
| 76 |
+
10,mlp.down_proj,0.0000026231,0.05000,3.068
|
| 77 |
+
10,self_attn.q_proj,0.0000101732,0.05000,2.230
|
| 78 |
+
10,self_attn.o_proj,0.0000007549,0.05000,2.262
|
| 79 |
+
10,self_attn.k_proj,0.0000127262,0.05000,2.365
|
| 80 |
+
10,self_attn.v_proj,0.0000108463,0.05000,2.387
|
| 81 |
+
11,mlp.up_proj,0.0000111043,0.05000,2.183
|
| 82 |
+
11,mlp.gate_proj,0.0000110840,0.05000,2.187
|
| 83 |
+
11,mlp.down_proj,0.0000028018,0.05000,3.373
|
| 84 |
+
11,self_attn.v_proj,0.0000078623,0.05000,2.592
|
| 85 |
+
11,self_attn.k_proj,0.0000090132,0.05000,2.733
|
| 86 |
+
11,self_attn.o_proj,0.0000008556,0.05000,2.759
|
| 87 |
+
11,self_attn.q_proj,0.0000073984,0.05000,2.772
|
| 88 |
+
12,mlp.gate_proj,0.0000107157,0.05000,2.134
|
| 89 |
+
12,mlp.up_proj,0.0000109496,0.05000,2.144
|
| 90 |
+
12,mlp.down_proj,0.0000028660,0.05000,3.327
|
| 91 |
+
12,self_attn.v_proj,0.0000108852,0.05000,2.999
|
| 92 |
+
12,self_attn.q_proj,0.0000093560,0.05000,3.022
|
| 93 |
+
12,self_attn.o_proj,0.0000010866,0.05000,3.033
|
| 94 |
+
12,self_attn.k_proj,0.0000102958,0.05000,3.039
|
| 95 |
+
13,mlp.up_proj,0.0000113037,0.05000,2.134
|
| 96 |
+
13,mlp.gate_proj,0.0000108564,0.05000,2.150
|
| 97 |
+
13,mlp.down_proj,0.0000030354,0.05000,3.340
|
| 98 |
+
13,self_attn.o_proj,0.0000011258,0.05000,2.630
|
| 99 |
+
13,self_attn.q_proj,0.0000071202,0.05000,2.637
|
| 100 |
+
13,self_attn.v_proj,0.0000072887,0.05000,2.655
|
| 101 |
+
13,self_attn.k_proj,0.0000090965,0.05000,2.658
|
| 102 |
+
14,mlp.gate_proj,0.0000102864,0.05000,2.116
|
| 103 |
+
14,mlp.up_proj,0.0000112935,0.05000,2.131
|
| 104 |
+
14,mlp.down_proj,0.0000033973,0.05000,3.311
|
| 105 |
+
14,self_attn.q_proj,0.0000077342,0.05000,2.791
|
| 106 |
+
14,self_attn.k_proj,0.0000088883,0.05000,2.801
|
| 107 |
+
14,self_attn.o_proj,0.0000014352,0.05000,2.806
|
| 108 |
+
14,self_attn.v_proj,0.0000076372,0.05000,2.810
|
| 109 |
+
15,mlp.up_proj,0.0000109415,0.05000,2.113
|
| 110 |
+
15,mlp.gate_proj,0.0000106577,0.05000,2.117
|
| 111 |
+
15,mlp.down_proj,0.0000029832,0.05000,3.304
|
| 112 |
+
15,self_attn.v_proj,0.0000053197,0.05000,2.740
|
| 113 |
+
15,self_attn.k_proj,0.0000064845,0.05000,2.750
|
| 114 |
+
15,self_attn.q_proj,0.0000050206,0.05000,2.756
|
| 115 |
+
15,self_attn.o_proj,0.0000019126,0.05000,2.758
|
| 116 |
+
16,mlp.up_proj,0.0000153010,0.05000,2.529
|
| 117 |
+
16,mlp.gate_proj,0.0000156530,0.05000,2.533
|
| 118 |
+
16,mlp.down_proj,0.0000057073,0.05000,3.721
|
| 119 |
+
16,self_attn.o_proj,failsafe(rtn): 0.0021057,0.00000,0.100
|
| 120 |
+
16,self_attn.q_proj,0.0000078978,0.05000,1.962
|
| 121 |
+
16,self_attn.k_proj,0.0000007718,0.05000,1.963
|
| 122 |
+
16,self_attn.v_proj,0.0000004404,0.05000,1.968
|
| 123 |
+
16,self_attn.o_gate,0.0000046133,0.05000,0.430
|
| 124 |
+
17,mlp.gate_proj,0.0000172837,0.05000,2.243
|
| 125 |
+
17,mlp.up_proj,0.0000157615,0.05000,2.252
|
| 126 |
+
17,mlp.down_proj,0.0000047822,0.05000,3.442
|
| 127 |
+
17,self_attn.o_proj,failsafe(rtn): 0.0021667,0.00000,0.103
|
| 128 |
+
17,self_attn.q_proj,0.0000081782,0.05000,1.955
|
| 129 |
+
17,self_attn.k_proj,0.0000007155,0.05000,1.958
|
| 130 |
+
17,self_attn.v_proj,0.0000006458,0.05000,1.977
|
| 131 |
+
17,self_attn.o_gate,0.0000054807,0.05000,0.410
|
| 132 |
+
18,mlp.up_proj,0.0000136515,0.05000,2.710
|
| 133 |
+
18,mlp.gate_proj,0.0000141853,0.05000,2.718
|
| 134 |
+
18,mlp.down_proj,0.0000036975,0.05000,3.911
|
| 135 |
+
18,self_attn.q_proj,0.0000070243,0.05000,3.069
|
| 136 |
+
18,self_attn.k_proj,0.0000099378,0.05000,3.079
|
| 137 |
+
18,self_attn.v_proj,0.0000070944,0.05000,3.091
|
| 138 |
+
18,self_attn.o_proj,0.0000013901,0.05000,3.093
|
| 139 |
+
19,mlp.up_proj,0.0000140069,0.05000,2.184
|
| 140 |
+
19,mlp.gate_proj,0.0000137057,0.05000,2.188
|
| 141 |
+
19,mlp.down_proj,0.0000041433,0.05000,3.382
|
| 142 |
+
19,self_attn.v_proj,0.0000092466,0.05000,2.955
|
| 143 |
+
19,self_attn.k_proj,0.0000115108,0.05000,2.976
|
| 144 |
+
19,self_attn.q_proj,0.0000087993,0.05000,2.978
|
| 145 |
+
19,self_attn.o_proj,0.0000013312,0.05000,2.981
|
| 146 |
+
20,mlp.gate_proj,0.0000136300,0.05000,2.360
|
| 147 |
+
20,mlp.up_proj,0.0000144002,0.05000,2.367
|
| 148 |
+
20,mlp.down_proj,0.0000054458,0.05000,3.551
|
| 149 |
+
20,self_attn.k_proj,0.0000093558,0.05000,2.430
|
| 150 |
+
20,self_attn.v_proj,0.0000069220,0.05000,2.485
|
| 151 |
+
20,self_attn.q_proj,0.0000069259,0.05000,2.488
|
| 152 |
+
20,self_attn.o_proj,0.0000021510,0.05000,2.491
|
| 153 |
+
21,mlp.up_proj,0.0000136941,0.05000,1.901
|
| 154 |
+
21,mlp.gate_proj,0.0000127453,0.05000,2.069
|
| 155 |
+
21,mlp.down_proj,0.0000064656,0.05000,3.211
|
| 156 |
+
21,self_attn.o_proj,0.0000032435,0.05000,2.564
|
| 157 |
+
21,self_attn.k_proj,0.0000082270,0.05000,2.652
|
| 158 |
+
21,self_attn.q_proj,0.0000072292,0.05000,2.698
|
| 159 |
+
21,self_attn.v_proj,0.0000077253,0.05000,2.702
|
| 160 |
+
22,mlp.gate_proj,0.0000178435,0.05000,2.226
|
| 161 |
+
22,mlp.up_proj,0.0000191504,0.05000,2.237
|
| 162 |
+
22,mlp.down_proj,0.0000115692,0.05000,3.429
|
| 163 |
+
22,self_attn.o_proj,failsafe(rtn): 0.0022430,0.00000,0.105
|
| 164 |
+
22,self_attn.q_proj,0.0000098795,0.05000,1.564
|
| 165 |
+
22,self_attn.v_proj,0.0000011001,0.05000,1.603
|
| 166 |
+
22,self_attn.k_proj,0.0000006362,0.05000,1.802
|
| 167 |
+
22,self_attn.o_gate,0.0000052467,0.05000,0.556
|
| 168 |
+
23,mlp.up_proj,0.0000205944,0.05000,2.568
|
| 169 |
+
23,mlp.gate_proj,0.0000191698,0.05000,2.574
|
| 170 |
+
23,mlp.down_proj,0.0000114501,0.05000,3.838
|
| 171 |
+
23,self_attn.k_proj,0.0000123230,0.05000,3.183
|
| 172 |
+
23,self_attn.v_proj,0.0000092833,0.05000,3.241
|
| 173 |
+
23,self_attn.q_proj,0.0000096182,0.05000,3.242
|
| 174 |
+
23,self_attn.o_proj,0.0000029686,0.05000,3.246
|
| 175 |
+
24,mlp.up_proj,0.0000243043,0.05000,2.174
|
| 176 |
+
24,mlp.gate_proj,0.0000224462,0.05000,2.178
|
| 177 |
+
24,mlp.down_proj,0.0000185971,0.05000,3.367
|
| 178 |
+
24,self_attn.o_proj,0.0000049888,0.05000,2.975
|
| 179 |
+
24,self_attn.v_proj,0.0000083148,0.05000,2.986
|
| 180 |
+
24,self_attn.k_proj,0.0000101886,0.05000,2.996
|
| 181 |
+
24,self_attn.q_proj,0.0000081873,0.05000,3.004
|
| 182 |
+
25,mlp.gate_proj,0.0000260207,0.05000,2.247
|
| 183 |
+
25,mlp.up_proj,0.0000286292,0.05000,2.265
|
| 184 |
+
25,mlp.down_proj,0.0000327358,0.05000,3.458
|
| 185 |
+
25,self_attn.k_proj,0.0000109056,0.05000,3.008
|
| 186 |
+
25,self_attn.o_proj,0.0000064882,0.05000,3.029
|
| 187 |
+
25,self_attn.v_proj,0.0000084491,0.05000,3.036
|
| 188 |
+
25,self_attn.q_proj,0.0000085720,0.05000,3.038
|
| 189 |
+
26,mlp.gate_proj,0.0000290269,0.05000,2.488
|
| 190 |
+
26,mlp.up_proj,0.0000328542,0.05000,2.502
|
| 191 |
+
26,mlp.down_proj,0.0000283380,0.05000,3.690
|
| 192 |
+
26,self_attn.o_proj,0.0000122008,0.05000,2.982
|
| 193 |
+
26,self_attn.k_proj,0.0000195720,0.05000,2.992
|
| 194 |
+
26,self_attn.q_proj,0.0000137304,0.05000,2.997
|
| 195 |
+
26,self_attn.v_proj,0.0000134189,0.05000,2.999
|
| 196 |
+
27,mlp.up_proj,0.0000379276,0.05000,2.567
|
| 197 |
+
27,mlp.gate_proj,0.0000329030,0.05000,2.567
|
| 198 |
+
27,mlp.down_proj,0.0000369176,0.05000,3.824
|
| 199 |
+
27,self_attn.v_proj,0.0000120097,0.05000,2.916
|
| 200 |
+
27,self_attn.q_proj,0.0000128456,0.05000,2.921
|
| 201 |
+
27,self_attn.o_proj,0.0000076826,0.05000,2.924
|
| 202 |
+
27,self_attn.k_proj,0.0000141221,0.05000,2.928
|
| 203 |
+
28,mlp.gate_proj,0.0000373178,0.05000,2.244
|
| 204 |
+
28,mlp.up_proj,0.0000440524,0.05000,2.267
|
| 205 |
+
28,mlp.down_proj,0.0000514231,0.05000,3.471
|
| 206 |
+
28,self_attn.k_proj,0.0000228180,0.05000,3.043
|
| 207 |
+
28,self_attn.v_proj,0.0000169691,0.05000,3.054
|
| 208 |
+
28,self_attn.q_proj,0.0000164989,0.05000,3.059
|
| 209 |
+
28,self_attn.o_proj,0.0000238898,0.05000,3.062
|
| 210 |
+
29,mlp.up_proj,0.0000564963,0.05000,2.573
|
| 211 |
+
29,mlp.gate_proj,0.0000464832,0.05000,2.598
|
| 212 |
+
29,mlp.down_proj,0.0000851531,0.05000,3.805
|
| 213 |
+
29,self_attn.o_proj,failsafe(rtn): 0.0025024,0.00000,0.113
|
| 214 |
+
29,self_attn.v_proj,0.0000107679,0.05000,2.037
|
| 215 |
+
29,self_attn.q_proj,0.0000211549,0.05000,2.046
|
| 216 |
+
29,self_attn.k_proj,0.0000011045,0.05000,2.075
|
| 217 |
+
29,self_attn.o_gate,0.0000234260,0.05000,0.413
|
| 218 |
+
30,mlp.gate_proj,0.0000589105,0.05000,2.553
|
| 219 |
+
30,mlp.up_proj,0.0000711039,0.05000,2.561
|
| 220 |
+
30,mlp.down_proj,0.0001752567,0.05000,3.852
|
| 221 |
+
30,self_attn.o_proj,failsafe(rtn): 0.0025940,0.00000,0.099
|
| 222 |
+
30,self_attn.k_proj,0.0000012830,0.05000,2.137
|
| 223 |
+
30,self_attn.q_proj,0.0000324827,0.05000,2.140
|
| 224 |
+
30,self_attn.v_proj,0.0000334367,0.05000,2.190
|
| 225 |
+
30,self_attn.o_gate,0.0000373244,0.05000,0.436
|
| 226 |
+
31,mlp.up_proj,0.0001038903,0.05000,2.291
|
| 227 |
+
31,mlp.gate_proj,0.0000930895,0.05000,2.361
|
| 228 |
+
31,mlp.down_proj,0.0007491196,0.05000,3.548
|
| 229 |
+
31,self_attn.o_proj,failsafe(rtn): 0.0024261,0.00000,0.107
|
| 230 |
+
31,self_attn.k_proj,0.0000008259,0.05000,2.147
|
| 231 |
+
31,self_attn.v_proj,0.0000051840,0.05000,2.153
|
| 232 |
+
31,self_attn.q_proj,0.0000221746,0.05000,2.155
|
| 233 |
+
31,self_attn.o_gate,0.0000232037,0.05000,0.409
|
quantize_config.json
ADDED
|
@@ -0,0 +1,54 @@
|
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|
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|
|
| 1 |
+
{
|
| 2 |
+
"bits": 4,
|
| 3 |
+
"dynamic": {
|
| 4 |
+
"+:model\\.model\\.layers\\.0\\..*": {
|
| 5 |
+
"bits": 8
|
| 6 |
+
},
|
| 7 |
+
"+:model\\.model\\.layers\\.31\\..*": {
|
| 8 |
+
"bits": 8
|
| 9 |
+
}
|
| 10 |
+
},
|
| 11 |
+
"group_size": 128,
|
| 12 |
+
"desc_act": false,
|
| 13 |
+
"lm_head": false,
|
| 14 |
+
"quant_method": "gptq",
|
| 15 |
+
"checkpoint_format": "gptq",
|
| 16 |
+
"pack_dtype": "int32",
|
| 17 |
+
"meta": {
|
| 18 |
+
"quantizer": [
|
| 19 |
+
"gptqmodel:5.7.0"
|
| 20 |
+
],
|
| 21 |
+
"uri": "https://github.com/modelcloud/gptqmodel",
|
| 22 |
+
"damp_percent": 0.05,
|
| 23 |
+
"damp_auto_increment": 0.01,
|
| 24 |
+
"static_groups": false,
|
| 25 |
+
"true_sequential": true,
|
| 26 |
+
"mse": 0.0,
|
| 27 |
+
"gptaq": null,
|
| 28 |
+
"act_group_aware": true,
|
| 29 |
+
"failsafe": {
|
| 30 |
+
"strategy": "rtn",
|
| 31 |
+
"threshold": "0.5%",
|
| 32 |
+
"smooth": {
|
| 33 |
+
"type": "mad",
|
| 34 |
+
"group_size_threshold": 128,
|
| 35 |
+
"k": 2.75
|
| 36 |
+
}
|
| 37 |
+
},
|
| 38 |
+
"offload_to_disk": true,
|
| 39 |
+
"offload_to_disk_path": "./gptqmodel_offload/hqdpgrum-rkaakpxx/",
|
| 40 |
+
"pack_impl": "cpu",
|
| 41 |
+
"mock_quantization": false,
|
| 42 |
+
"gc_mode": "interval",
|
| 43 |
+
"wait_for_submodule_finalizers": false,
|
| 44 |
+
"auto_forward_data_parallel": true,
|
| 45 |
+
"hessian": {
|
| 46 |
+
"chunk_size": null,
|
| 47 |
+
"chunk_bytes": null,
|
| 48 |
+
"staging_dtype": "float32"
|
| 49 |
+
},
|
| 50 |
+
"vram_strategy": "exclusive"
|
| 51 |
+
},
|
| 52 |
+
"sym": true,
|
| 53 |
+
"format": "gptq"
|
| 54 |
+
}
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,46 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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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 |
+
{
|
| 2 |
+
"additional_special_tokens": [
|
| 3 |
+
"<|im_end|>",
|
| 4 |
+
"<|im_start|>",
|
| 5 |
+
"<tool_call>",
|
| 6 |
+
"</tool_call>",
|
| 7 |
+
"<|im_sep|>",
|
| 8 |
+
"<|fim_prefix|>",
|
| 9 |
+
"<|fim_middle|>",
|
| 10 |
+
"<|fim_suffix|>",
|
| 11 |
+
"<tool_response>",
|
| 12 |
+
"</tool_response>",
|
| 13 |
+
"<tools>",
|
| 14 |
+
"</tools>",
|
| 15 |
+
"<arguments>",
|
| 16 |
+
"</arguments>",
|
| 17 |
+
"<parameters>",
|
| 18 |
+
"</parameters>",
|
| 19 |
+
"<function",
|
| 20 |
+
"</function>",
|
| 21 |
+
"<param",
|
| 22 |
+
"</param>"
|
| 23 |
+
],
|
| 24 |
+
"bos_token": {
|
| 25 |
+
"content": "<s>",
|
| 26 |
+
"lstrip": false,
|
| 27 |
+
"normalized": false,
|
| 28 |
+
"rstrip": false,
|
| 29 |
+
"single_word": false
|
| 30 |
+
},
|
| 31 |
+
"eos_token": {
|
| 32 |
+
"content": "<|im_end|>",
|
| 33 |
+
"lstrip": false,
|
| 34 |
+
"normalized": false,
|
| 35 |
+
"rstrip": false,
|
| 36 |
+
"single_word": false
|
| 37 |
+
},
|
| 38 |
+
"pad_token": "</s>",
|
| 39 |
+
"unk_token": {
|
| 40 |
+
"content": "<unk>",
|
| 41 |
+
"lstrip": false,
|
| 42 |
+
"normalized": false,
|
| 43 |
+
"rstrip": false,
|
| 44 |
+
"single_word": false
|
| 45 |
+
}
|
| 46 |
+
}
|
tokenizer.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
tokenizer.model
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:bb74d51116831c3bf65db812c553f94ab0c88dcf97a5bbb37e3504f6d359c530
|
| 3 |
+
size 1181204
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,242 @@
|
|
|
|
|
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|
|
|
|
| 1 |
+
{
|
| 2 |
+
"add_bos_token": true,
|
| 3 |
+
"add_eos_token": false,
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| 4 |
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