Text Generation
Transformers
Safetensors
English
Chinese
llama
minicpm
minicpm5
long-context
tool-calling
on-device
edge-ai
conversational
text-generation-inference
Instructions to use openbmb/MiniCPM5-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/MiniCPM5-1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="openbmb/MiniCPM5-1B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("openbmb/MiniCPM5-1B") model = AutoModelForCausalLM.from_pretrained("openbmb/MiniCPM5-1B") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use openbmb/MiniCPM5-1B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "openbmb/MiniCPM5-1B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "openbmb/MiniCPM5-1B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/openbmb/MiniCPM5-1B
- SGLang
How to use openbmb/MiniCPM5-1B with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "openbmb/MiniCPM5-1B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "openbmb/MiniCPM5-1B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "openbmb/MiniCPM5-1B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "openbmb/MiniCPM5-1B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use openbmb/MiniCPM5-1B with Docker Model Runner:
docker model run hf.co/openbmb/MiniCPM5-1B
init commit
Browse files- README.md +0 -0
- chat_template.jinja +179 -0
- config.json +30 -0
- generation_config.json +10 -0
- model-00000-of-00001.safetensors +3 -0
- model.safetensors.index.json +226 -0
- special_tokens_map.json +30 -0
- tokenizer.json +0 -0
- tokenizer_config.json +0 -0
README.md
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chat_template.jinja
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| 1 |
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{{- bos_token }}{%- if tools %}
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| 2 |
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{%- set tool_definitions %}
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| 3 |
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{{- "# Tools\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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| 4 |
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{%- for tool in tools %}
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{{- "\n" }}
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| 6 |
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{{- tool | tojson(ensure_ascii=False) }}
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| 7 |
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{%- endfor %}
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| 8 |
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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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| 9 |
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{%- endset %}
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| 10 |
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| 11 |
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{{- '<|im_start|>system\n' }}
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| 12 |
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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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| 14 |
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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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| 18 |
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{%- else %}
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| 19 |
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{{- tool_definitions.lstrip() }}
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| 20 |
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{%- endif %}
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{{- '<|im_end|>\n' }}
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| 22 |
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{%- else %}
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{%- if messages[0].role == 'system' %}
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| 24 |
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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| 25 |
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{%- endif %}
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| 26 |
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{%- endif %}
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| 27 |
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for message in messages[::-1] %}
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{%- set index = (messages|length - 1) - loop.index0 %}
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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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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
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{%- endif %}
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{%- endfor %}
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{%- for message in messages %}
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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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{%- set content = '' %}
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{%- endif %}
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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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{%- elif message.role == "assistant" %}
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{%- set reasoning_content = '' %}
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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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{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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{%- set content = content.split('</think>')[-1].lstrip('\n') %}
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{%- endif %}
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{%- endif %}
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{%- if message.tool_calls %}
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{%- set content_parts = content.split('<tool_sep>') %}
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{%- set processed_content = content_parts[0] %}
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{%- set tool_calls_count = message.tool_calls|length %}
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{%- set tool_sep_count = content_parts|length - 1 %}
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{%- set min_count = [tool_calls_count, tool_sep_count]|min %}
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{%- for i in range(1, content_parts|length) %}
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{%- set tool_index = i - 1 %}
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{%- if tool_index < tool_calls_count %}
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{%- set tool_call = message.tool_calls[tool_index] %}
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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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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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{%- if tool_call.arguments %}
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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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{{- '<![CDATA[' + param_value + ']]>' }}
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| 76 |
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{%- else %}
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{{- param_value }}
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| 78 |
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{%- endif %}
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{{- '</param>' }}
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| 80 |
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{%- endfor %}
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| 81 |
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{%- endif %}
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| 82 |
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{{- '</function>' }}
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| 83 |
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{%- endset %}
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| 84 |
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{%- 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 |
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{%- set processed_content = processed_content + content_parts[i] %}
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| 87 |
+
{%- endif %}
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| 88 |
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{%- endfor %}
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| 89 |
+
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| 90 |
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{%- 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 |
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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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| 95 |
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{%- endif %}
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{%- set remaining_tool_xml %}
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| 97 |
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{{- '<function name="' ~ tool_call.name ~ '">' }}
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| 98 |
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{%- if tool_call.arguments %}
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| 99 |
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{%- set args_dict = tool_call.arguments %}
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| 100 |
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{%- 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 |
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{{- '<![CDATA[' + param_value + ']]>' }}
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| 104 |
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{%- else %}
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{{- param_value }}
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| 106 |
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{%- endif %}
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{{- '</param>' }}
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{%- endfor %}
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| 109 |
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{%- endif %}
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{{- '</function>' }}
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| 111 |
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{%- endset %}
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| 112 |
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{%- set processed_content = processed_content + remaining_tool_xml %}
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| 113 |
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{%- endfor %}
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| 114 |
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{%- endif %}
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| 115 |
+
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| 116 |
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{%- set content = processed_content %}
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| 117 |
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{%- endif %}
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| 118 |
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{%- if loop.index0 > ns.last_query_index %}
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{%- if reasoning_content %}
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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| 122 |
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{%- else %}
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| 123 |
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{{- '<|im_start|>' + message.role + '\n' + content }}
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| 124 |
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{%- endif %}
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| 125 |
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{%- else %}
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| 126 |
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{{- '<|im_start|>' + message.role + '\n' + content }}
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| 127 |
+
{%- endif %}
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| 128 |
+
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| 129 |
+
{%- if message.tool_calls and not has_tool_sep %}
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| 130 |
+
{%- for tool_call in message.tool_calls %}
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| 131 |
+
{%- if (loop.first and content) or (not loop.first) %}
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| 132 |
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{{- '\n' }}
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| 133 |
+
{%- endif %}
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| 134 |
+
{%- if tool_call.function %}
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| 135 |
+
{%- set tool_call = tool_call.function %}
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| 136 |
+
{%- endif %}
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| 137 |
+
{{- '<function name="' ~ tool_call.name ~ '">' }}
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| 138 |
+
{%- if tool_call.arguments %}
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| 139 |
+
{%- set args_dict = tool_call.arguments %}
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| 140 |
+
{%- for param_name, param_value in args_dict.items() %}
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| 141 |
+
{{- '<param name="' ~ param_name ~ '">' }}
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| 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 + ']]>' }}
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| 144 |
+
{%- else %}
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| 145 |
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{{- param_value }}
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| 146 |
+
{%- endif %}
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| 147 |
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{{- '</param>' }}
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| 148 |
+
{%- endfor %}
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| 149 |
+
{%- endif %}
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| 150 |
+
{{- '</function>' }}
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| 151 |
+
{%- endfor %}
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| 152 |
+
{%- endif %}
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| 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") %}
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| 156 |
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{{- '<|im_start|>user' }}
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| 157 |
+
{%- endif %}
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| 158 |
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{{- '\n<tool_response>\n' }}
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| 159 |
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{%- if message.content is string %}
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| 160 |
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{{- content }}
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| 161 |
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{%- else %}
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| 162 |
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{{- message.content | tojson(ensure_ascii=False) }}
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| 163 |
+
{%- endif %}
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| 164 |
+
{{- '\n</tool_response>' }}
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| 165 |
+
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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| 166 |
+
{{- '<|im_end|>\n' }}
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| 167 |
+
{%- endif %}
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| 168 |
+
{%- endif %}
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| 169 |
+
{%- endfor %}
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| 170 |
+
{%- if add_generation_prompt %}
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| 171 |
+
{{- '<|im_start|>assistant\n' }}
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| 172 |
+
{%- if enable_thinking is defined %}
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| 173 |
+
{%- if enable_thinking is false %}
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| 174 |
+
{{- '<think>\n\n</think>\n\n' }}
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| 175 |
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{%- elif enable_thinking is true %}
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| 176 |
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{{- '<think>\n' }}
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| 177 |
+
{%- endif %}
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| 178 |
+
{%- endif %}
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| 179 |
+
{%- endif %}
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config.json
ADDED
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@@ -0,0 +1,30 @@
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| 1 |
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{
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| 2 |
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"_name_or_path": "openbmb/MiniCPM5-0.9B",
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| 3 |
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"architectures": [
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| 4 |
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"LlamaForCausalLM"
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| 5 |
+
],
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| 6 |
+
"bos_token_id": 0,
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| 7 |
+
"eos_token_id": [
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| 8 |
+
1,
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| 9 |
+
130073
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| 10 |
+
],
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| 11 |
+
"pad_token_id": 1,
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| 12 |
+
"hidden_act": "silu",
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| 13 |
+
"hidden_size": 1536,
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| 14 |
+
"initializer_range": 0.02,
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| 15 |
+
"intermediate_size": 4608,
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| 16 |
+
"max_position_embeddings": 131072,
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| 17 |
+
"model_type": "llama",
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| 18 |
+
"num_attention_heads": 16,
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| 19 |
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"num_hidden_layers": 24,
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| 20 |
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"num_key_value_heads": 2,
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| 21 |
+
"head_dim": 128,
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| 22 |
+
"rms_norm_eps": 1e-06,
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| 23 |
+
"rope_theta": 5000000,
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| 24 |
+
"rope_scaling": null,
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| 25 |
+
"torch_dtype": "bfloat16",
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| 26 |
+
"transformers_version": "4.36.0",
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| 27 |
+
"use_cache": true,
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| 28 |
+
"vocab_size": 130560,
|
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|
| 225 |
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|
| 226 |
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special_tokens_map.json
ADDED
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|
| 1 |
+
{
|
| 2 |
+
"bos_token": {
|
| 3 |
+
"content": "<s>",
|
| 4 |
+
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|
| 5 |
+
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|
| 6 |
+
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|
| 7 |
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|
| 8 |
+
},
|
| 9 |
+
"eos_token": {
|
| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
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|
| 14 |
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|
| 15 |
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},
|
| 16 |
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"pad_token": {
|
| 17 |
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"content": "</s>",
|
| 18 |
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|
| 19 |
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|
| 20 |
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|
| 21 |
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"single_word": false
|
| 22 |
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|
| 23 |
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"unk_token": {
|
| 24 |
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|
| 25 |
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|
| 26 |
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|
| 27 |
+
"rstrip": false,
|
| 28 |
+
"single_word": false
|
| 29 |
+
}
|
| 30 |
+
}
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tokenizer.json
ADDED
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tokenizer_config.json
ADDED
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