Text Generation
Transformers
Safetensors
minimax_m2
neuralmagic
redhat
llmcompressor
quantized
INT4
conversational
custom_code
compressed-tensors
Instructions to use RedHatAI/MiniMax-M2.5-quantized.w4a16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RedHatAI/MiniMax-M2.5-quantized.w4a16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RedHatAI/MiniMax-M2.5-quantized.w4a16", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("RedHatAI/MiniMax-M2.5-quantized.w4a16", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("RedHatAI/MiniMax-M2.5-quantized.w4a16", trust_remote_code=True) 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 RedHatAI/MiniMax-M2.5-quantized.w4a16 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RedHatAI/MiniMax-M2.5-quantized.w4a16" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RedHatAI/MiniMax-M2.5-quantized.w4a16", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/RedHatAI/MiniMax-M2.5-quantized.w4a16
- SGLang
How to use RedHatAI/MiniMax-M2.5-quantized.w4a16 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 "RedHatAI/MiniMax-M2.5-quantized.w4a16" \ --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": "RedHatAI/MiniMax-M2.5-quantized.w4a16", "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 "RedHatAI/MiniMax-M2.5-quantized.w4a16" \ --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": "RedHatAI/MiniMax-M2.5-quantized.w4a16", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use RedHatAI/MiniMax-M2.5-quantized.w4a16 with Docker Model Runner:
docker model run hf.co/RedHatAI/MiniMax-M2.5-quantized.w4a16
File size: 6,524 Bytes
9e39fcc | 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 | {# ----------‑‑‑ special token variables ‑‑‑---------- #}
{%- set toolcall_begin_token = '<minimax:tool_call>' -%}
{%- set toolcall_end_token = '</minimax:tool_call>' -%}
{#- Tool Rendering Functions ============================================== -#}
{%- macro render_tool_namespace(namespace_name, tool_list) -%}
{%- for tool in tool_list -%}
<tool>{{ tool.function | tojson(ensure_ascii=False) }}</tool>
{% endfor -%}
{%- endmacro -%}
{%- macro visible_text(content) -%}
{%- if content is string -%}
{{ content }}
{%- elif content is iterable and content is not mapping -%}
{%- for item in content -%}
{%- if item is mapping and item.type == 'text' -%}
{{- item.text }}
{%- elif item is string -%}
{{- item }}
{%- endif -%}
{%- endfor -%}
{%- else -%}
{{- content }}
{%- endif -%}
{%- endmacro -%}
{#- System Message Construction ============================================ -#}
{%- macro build_system_message(system_message) -%}
{%- if system_message and system_message.content -%}
{{- visible_text(system_message.content) }}
{%- else -%}
{%- if model_identity is not defined -%}
{%- set model_identity = "You are a helpful assistant. Your name is MiniMax-M2.5 and is built by MiniMax." -%}
{%- endif -%}
{{- model_identity }}
{%- endif -%}
{#- Handle current_date -#}
{%- if system_message and system_message.current_date -%}
{{- '\n' ~ 'Current date: ' + system_message.current_date }}
{%- endif -%}
{#- Handle current_location -#}
{%- if system_message and system_message.current_location -%}
{{- '\n' ~ 'Current location: ' + system_message.current_location }}
{%- endif -%}
{%- endmacro -%}
{#- Main Template Logic ================================================= -#}
{#- Extract system message (only first message if it's system) -#}
{%- set system_message = none -%}
{%- set conversation_messages = messages -%}
{%- if messages and messages[0].role == "system" -%}
{%- set system_message = messages[0] -%}
{%- set conversation_messages = messages[1:] -%}
{%- endif -%}
{#- Get the last user message turn, for interleved thinking -#}
{%- set ns = namespace(last_user_index=-1) %}
{% for m in conversation_messages %}
{%- if m.role == 'user' %}
{% set ns.last_user_index = loop.index0 -%}
{%- endif %}
{%- endfor %}
{#- Render system message -#}
{{- ']~!b[' ~ ']~b]system' ~ '\n' }}
{{- build_system_message(system_message) }}
{#- Render tools if available -#}
{%- if tools -%}
{{- '\n\n' ~ '# Tools' ~ '\n' ~ 'You may call one or more tools to assist with the user query.\nHere are the tools available in JSONSchema format:' ~ '\n' }}
{{- '\n' ~ '<tools>' ~ '\n' }}
{{- render_tool_namespace("functions", tools) }}
{{- '</tools>' ~ '\n\n' }}
{{- 'When making tool calls, use XML format to invoke tools and pass parameters:' ~ '\n' }}
{{- '\n' ~ toolcall_begin_token }}
<invoke name="tool-name-1">
<parameter name="param-key-1">param-value-1</parameter>
<parameter name="param-key-2">param-value-2</parameter>
...
</invoke>
{{- '\n' ~ toolcall_end_token }}
{%- endif -%}
{{- '[e~[\n' }}
{#- Render messages -#}
{%- set last_tool_call = namespace(name=none) -%}
{%- for message in conversation_messages -%}
{%- if message.role == 'assistant' -%}
{#- Only render reasoning_content if no user message follows -#}
{{- ']~b]ai' ~ '\n' }}
{%- set reasoning_content = '' %}
{%- set content = visible_text(message.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].strip('\n').split('<think>')[-1].strip('\n') %}
{%- set content = content.split('</think>')[-1].strip('\n') %}
{%- endif %}
{%- endif %}
{%- if reasoning_content and loop.index0 > ns.last_user_index -%}
{{- '<think>' ~ '\n' ~ reasoning_content ~ '\n' ~ '</think>' ~ '\n\n' }}
{%- endif -%}
{%- if content -%}
{{- content }}
{%- endif -%}
{%- if message.tool_calls -%}
{{- '\n' ~ toolcall_begin_token ~ '\n' }}
{%- for tool_call in message.tool_calls -%}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<invoke name="' + tool_call.name + '">' }}
{% set _args = tool_call.arguments %}
{%- for k, v in _args.items() %}
{{- '<parameter name="' + k + '">' }}
{{- v | tojson(ensure_ascii=False) if v is not string else v }}
{{- '</parameter>' }}
{% endfor %}
{{- '</invoke>' ~ '\n' }}
{%- endfor -%}
{{- toolcall_end_token}}
{%- set last_tool_call.name = message.tool_calls[-1].name -%}
{%- else -%}
{%- set last_tool_call.name = none -%}
{%- endif -%}
{{- '[e~[' ~ '\n' }}
{%- elif message.role == 'tool' -%}
{%- if last_tool_call.name is none -%}
{{- raise_exception("Message has tool role, but there was no previous assistant message with a tool call!") }}
{%- endif -%}
{%- if loop.first or (conversation_messages[loop.index0 - 1].role != 'tool') -%}
{{- ']~b]tool' }}
{%- endif -%}
{%- if message.content is string -%}
{{- '\n<response>' }}
{{- message.content }}
{{- '</response>' }}
{%- else -%}
{%- for tr in message.content -%}
{{- '\n<response>' }}
{{- tr.output if tr.output is defined else (tr.text if tr.type == 'text' and tr.text is defined else tr) }}
{{- '\n</response>' }}
{%- endfor -%}
{%- endif -%}
{%- if loop.last or (conversation_messages[loop.index0 + 1].role != 'tool') -%}
{{- '[e~[\n' -}}
{%- endif -%}
{%- elif message.role == 'user' -%}
{{- ']~b]user' ~ '\n' }}
{{- visible_text(message.content) }}
{{- '[e~[' ~ '\n' }}
{%- endif -%}
{%- endfor -%}
{#- Generation prompt -#}
{%- if add_generation_prompt -%}
{{- ']~b]ai' ~ '\n' ~ '<think>' ~ '\n' }}
{%- endif -%}
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