Text Generation
Transformers
Safetensors
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minicpm
minicpm5
long-context
tool-calling
on-device
edge-ai
conversational
text-generation-inference
Instructions to use openbmb/MiniCPM5-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/MiniCPM5-2B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="openbmb/MiniCPM5-2B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("openbmb/MiniCPM5-2B") model = AutoModelForCausalLM.from_pretrained("openbmb/MiniCPM5-2B", device_map="auto") 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use openbmb/MiniCPM5-2B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "openbmb/MiniCPM5-2B" # 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/MiniCPM5-2B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/openbmb/MiniCPM5-2B
- SGLang
How to use openbmb/MiniCPM5-2B 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/MiniCPM5-2B" \ --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/MiniCPM5-2B", "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/MiniCPM5-2B" \ --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/MiniCPM5-2B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use openbmb/MiniCPM5-2B with Docker Model Runner:
docker model run hf.co/openbmb/MiniCPM5-2B
Listo
#16
by eduardoluis - opened
- README-cn.md +2 -0
- chat_template.jinja +66 -1
README-cn.md
CHANGED
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@@ -370,6 +370,8 @@ outputs = model.generate(**inputs, max_new_tokens=128)
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print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
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```
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## 工具调用
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工具调用**推荐使用 SGLang**。MiniCPM5-2B 以 XML 格式产出工具调用,SGLang 内置的 `minicpm5` parser 会自动将其转换为 OpenAI 兼容的 `tool_calls` 字段。
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print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
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```
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+
推荐的采样参数:`temperature=1.0, top_p=0.95`
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+
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## 工具调用
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工具调用**推荐使用 SGLang**。MiniCPM5-2B 以 XML 格式产出工具调用,SGLang 内置的 `minicpm5` parser 会自动将其转换为 OpenAI 兼容的 `tool_calls` 字段。
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chat_template.jinja
CHANGED
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@@ -51,6 +51,71 @@
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{%- endif %}
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{%- endif %}
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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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{%- elif '<think>' not in content and '</think>' not in content %}
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@@ -59,7 +124,7 @@
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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-
{%- if message.tool_calls %}
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{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{{- '\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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{%- for param_name, param_value in args_dict.items() %}
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{{- '<param name="' ~ param_name ~ '">' }}
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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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{%- else %}
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{{- param_value }}
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{%- endif %}
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{{- '</param>' }}
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{%- endfor %}
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{%- endif %}
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{{- '</function>' }}
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{%- endset %}
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{%- set processed_content = processed_content + single_tool_xml + content_parts[i] %}
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{%- else %}
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{%- set processed_content = processed_content + content_parts[i] %}
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{%- endif %}
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{%- endfor %}
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{%- if tool_calls_count > tool_sep_count %}
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{%- for remaining_index in range(tool_sep_count, tool_calls_count) %}
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{%- set tool_call = message.tool_calls[remaining_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 remaining_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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{%- for param_name, param_value in args_dict.items() %}
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{{- '<param name="' ~ param_name ~ '">' }}
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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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{%- else %}
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{{- param_value }}
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{%- endif %}
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{{- '</param>' }}
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{%- endfor %}
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{%- endif %}
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{{- '</function>' }}
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{%- endset %}
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{%- set processed_content = processed_content + remaining_tool_xml %}
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{%- endfor %}
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{%- endif %}
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{%- set content = processed_content %}
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{%- endif %}
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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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{%- elif '<think>' not in content and '</think>' not in content %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- if message.tool_calls and not has_tool_sep %}
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{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{{- '\n' }}
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