Text Generation
Transformers
Safetensors
English
Chinese
llama
minicpm
minicpm5
long-context
tool-calling
on-device
edge-ai
conversational
text-generation-inference
4-bit precision
Instructions to use openbmb/MiniCPM5-1B-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/MiniCPM5-1B-MLX with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="openbmb/MiniCPM5-1B-MLX") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("openbmb/MiniCPM5-1B-MLX") model = AutoModelForCausalLM.from_pretrained("openbmb/MiniCPM5-1B-MLX") 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-MLX with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "openbmb/MiniCPM5-1B-MLX" # 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-MLX", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/openbmb/MiniCPM5-1B-MLX
- SGLang
How to use openbmb/MiniCPM5-1B-MLX 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-MLX" \ --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-MLX", "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-MLX" \ --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-MLX", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use openbmb/MiniCPM5-1B-MLX with Docker Model Runner:
docker model run hf.co/openbmb/MiniCPM5-1B-MLX
File size: 9,062 Bytes
92b4f50 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 | {{- bos_token }}{%- if tools %}
{%- set tool_definitions %}
{{- "# Tools\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson(ensure_ascii=False) }}
{%- endfor %}
{{- '\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>' }}
{%- endset %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{%- if '<tool_def_sep>' in messages[0].content %}
{{- messages[0].content.replace('<tool_def_sep>', tool_definitions) }}
{%- else %}
{{- messages[0].content + '\n\n' + tool_definitions }}
{%- endif %}
{%- else %}
{{- tool_definitions.lstrip() }}
{%- endif %}
{{- '<|im_end|>\n' }}
{%- else %}
{%- if messages[0].role == 'system' %}
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
{%- for message in messages[::-1] %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- 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>')) %}
{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = index %}
{%- endif %}
{%- endfor %}
{%- for message in messages %}
{%- if message.content is string %}
{%- set content = message.content %}
{%- else %}
{%- set content = '' %}
{%- endif %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is string %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in content %}
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if message.tool_calls %}
{%- set content_parts = content.split('<tool_sep>') %}
{%- set processed_content = content_parts[0] %}
{%- set tool_calls_count = message.tool_calls|length %}
{%- set tool_sep_count = content_parts|length - 1 %}
{%- set min_count = [tool_calls_count, tool_sep_count]|min %}
{%- for i in range(1, content_parts|length) %}
{%- set tool_index = i - 1 %}
{%- if tool_index < tool_calls_count %}
{%- set tool_call = message.tool_calls[tool_index] %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{%- set single_tool_xml %}
{{- '<function name="' ~ tool_call.name ~ '">' }}
{%- if tool_call.arguments %}
{%- set args_dict = tool_call.arguments %}
{%- for param_name, param_value in args_dict.items() %}
{{- '<param name="' ~ param_name ~ '">' }}
{%- if param_value is string and ('<' in param_value or '&' in param_value or '\n' in param_value) %}
{{- '<![CDATA[' + param_value + ']]>' }}
{%- else %}
{{- param_value }}
{%- endif %}
{{- '</param>' }}
{%- endfor %}
{%- endif %}
{{- '</function>' }}
{%- endset %}
{%- set processed_content = processed_content + single_tool_xml + content_parts[i] %}
{%- else %}
{%- set processed_content = processed_content + content_parts[i] %}
{%- endif %}
{%- endfor %}
{%- if tool_calls_count > tool_sep_count %}
{%- for remaining_index in range(tool_sep_count, tool_calls_count) %}
{%- set tool_call = message.tool_calls[remaining_index] %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{%- set remaining_tool_xml %}
{{- '<function name="' ~ tool_call.name ~ '">' }}
{%- if tool_call.arguments %}
{%- set args_dict = tool_call.arguments %}
{%- for param_name, param_value in args_dict.items() %}
{{- '<param name="' ~ param_name ~ '">' }}
{%- if param_value is string and ('<' in param_value or '&' in param_value or '\n' in param_value) %}
{{- '<![CDATA[' + param_value + ']]>' }}
{%- else %}
{{- param_value }}
{%- endif %}
{{- '</param>' }}
{%- endfor %}
{%- endif %}
{{- '</function>' }}
{%- endset %}
{%- set processed_content = processed_content + remaining_tool_xml %}
{%- endfor %}
{%- endif %}
{%- set content = processed_content %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if reasoning_content %}
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- if message.tool_calls and not has_tool_sep %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '\n' }}
{%- endif %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<function name="' ~ tool_call.name ~ '">' }}
{%- if tool_call.arguments %}
{%- set args_dict = tool_call.arguments %}
{%- for param_name, param_value in args_dict.items() %}
{{- '<param name="' ~ param_name ~ '">' }}
{%- if param_value is string and ('<' in param_value or '&' in param_value or '\n' in param_value) %}
{{- '<![CDATA[' + param_value + ']]>' }}
{%- else %}
{{- param_value }}
{%- endif %}
{{- '</param>' }}
{%- endfor %}
{%- endif %}
{{- '</function>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{%- if message.content is string %}
{{- content }}
{%- else %}
{{- message.content | tojson(ensure_ascii=False) }}
{%- endif %}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- if enable_thinking is defined %}
{%- if enable_thinking is false %}
{{- '<think>\n\n</think>\n\n' }}
{%- elif enable_thinking is true %}
{{- '<think>\n' }}
{%- endif %}
{%- endif %}
{%- endif %}
|