FEAT: [model] glm-5 support

xinference/model/llm/llm_family.json

@@ -26202,5 +26202,61 @@
     },
     "featured": false,
     "updated_at": 1772095707
+  },
+  {
+    "model_name": "glm-5",
+    "model_description": "We are launching GLM-5, targeting complex systems engineering and long-horizon agentic tasks. Scaling is still one of the most important ways to improve the intelligence efficiency of Artificial General Intelligence (AGI). Compared to GLM-4.5, GLM-5 scales from 355B parameters (32B active) to 744B parameters (40B active), and increases pre-training data from 23T to 28.5T tokens. GLM-5 also integrates DeepSeek Sparse Attention (DSA), largely reducing deployment cost while preserving long-context capacity.  Reinforcement learning aims to bridge the gap between competence and excellence in pre-trained models. However, deploying it at scale for LLMs is a challenge due to the RL training inefficiency. To this end, we developed slime, a novel asynchronous RL infrastructure that substantially improves training throughput and efficiency, enabling more fine-grained post-training iterations. With advances in both pre-training and post-training, GLM-5 delivers significant improvement compared to GLM-4.7 across a wide range of academic benchmarks and achieves best-in-class performance among all open-source models in the world on reasoning, coding, and agentic tasks, closing the gap with frontier models.",
+    "context_length": 202752,
+    "model_lang": [
+      "en",
+      "zh"
+    ],
+    "model_ability": [
+      "chat",
+      "vision",
+      "tools",
+      "reasoning"
+    ],
+    "model_specs": [
+      {
+        "model_size_in_billions": 744,
+        "model_format": "pytorch",
+        "model_src": {
+          "huggingface": {
+            "model_id": "zai-org/GLM-5"
+          },
+          "modelscope": {
+            "model_id": "ZhipuAI/GLM-5"
+          }
+        }
+      }
+    ],
+    "architectures": [
+      "GlmMoeDsaForCausalLM"
+    ],
+    "chat_template": "[gMASK]<sop>\n{%- if tools -%}\n<|system|>\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>\n{% for tool in tools %}\n{{ tool | tojson(ensure_ascii=False) }}\n{% endfor %}\n</tools>\n\nFor each function call, output the function name and arguments within the following XML format:\n<tool_call>{function-name}<arg_key>{arg-key-1}</arg_key><arg_value>{arg-value-1}</arg_value><arg_key>{arg-key-2}</arg_key><arg_value>{arg-value-2}</arg_value>...</tool_call>{%- endif -%}\n{%- macro visible_text(content) -%}\n    {%- if content is string -%}\n        {{- content }}\n    {%- elif content is iterable and content is not mapping -%}\n        {%- for item in content -%}\n            {%- if item is mapping and item.type == 'text' -%}\n                {{- item.text }}\n            {%- elif item is string -%}\n                {{- item }}\n            {%- endif -%}\n        {%- endfor -%}\n    {%- else -%}\n        {{- content }}\n    {%- endif -%}\n{%- endmacro -%}\n{%- set ns = namespace(last_user_index=-1) %}\n{%- for m in messages %}\n    {%- if m.role == 'user' %}\n        {% set ns.last_user_index = loop.index0 -%}\n    {%- endif %}\n{%- endfor %}\n{% for m in messages %}\n{%- if m.role == 'user' -%}<|user|>{{ visible_text(m.content) }}\n{%- elif m.role == 'assistant' -%}\n<|assistant|>\n{%- set reasoning_content = '' %}\n{%- set content = visible_text(m.content) %}\n{%- if m.reasoning_content is string %}\n    {%- set reasoning_content = m.reasoning_content %}\n{%- else %}\n    {%- if '</think>' in content %}\n        {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n        {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n    {%- endif %}\n{%- endif %}\n{%- if ((clear_thinking is defined and not clear_thinking) or loop.index0 > ns.last_user_index) and reasoning_content -%}\n{{ '<think>' + reasoning_content.strip() +  '</think>'}}\n{%- else -%}\n{{ '</think>' }}\n{%- endif -%}\n{%- if content.strip() -%}\n{{ content.strip() }}\n{%- endif -%}\n{% if m.tool_calls %}\n{% for tc in m.tool_calls %}\n{%- if tc.function %}\n    {%- set tc = tc.function %}\n{%- endif %}\n{{- '<tool_call>' + tc.name -}}\n{% set _args = tc.arguments %}{% for k, v in _args.items() %}<arg_key>{{ k }}</arg_key><arg_value>{{ v | tojson(ensure_ascii=False) if v is not string else v }}</arg_value>{% endfor %}</tool_call>{% endfor %}\n{% endif %}\n{%- elif m.role == 'tool' -%}\n{%- if m.content is string -%}\n{%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n    {{- '<|observation|>' }}\n{%- endif %}\n{{- '<tool_response>' }}\n{{- m.content }}\n{{- '</tool_response>' }}\n{%- else -%}\n<|observation|>{% for tr in m.content %}\n<tool_response>{{ tr.output if tr.output is defined else tr }}</tool_response>{% endfor -%}\n{% endif -%}\n{%- elif m.role == 'system' -%}\n<|system|>{{ visible_text(m.content) }}\n{%- endif -%}\n{%- endfor -%}\n{%- if add_generation_prompt -%}\n    <|assistant|>{{- '</think>' if (enable_thinking is defined and not enable_thinking) else '<think>' -}}\n{%- endif -%}",
+    "stop_token_ids": [
+      154820,
+      154827,
+      154829
+    ],
+    "stop": [
+      "<|endoftext|>"
+    ],
+    "tool_parser": "glm4",
+    "reasoning_start_tag": "<think>",
+    "reasoning_end_tag": "</think>",
+    "version": 2,
+    "virtualenv": {
+      "packages": [
+        "#transformers_dependencies# ; #engine# == \"Transformers\"",
+        "#mlx_dependencies# ; #engine# == \"MLX\"",
+        "#vllm_dependencies# ; #engine# == \"vllm\"",
+        "#sglang_dependencies# ; #engine# == \"sglang\"",
+        "#system_numpy# ; #engine# == \"vllm\""
+      ]
+    },
+    "featured": false,
+    "updated_at": 1772247655
   }
 ]