> ## Documentation Index > Fetch the complete documentation index at: https://docs.baseten.co/llms.txt > Use this file to discover all available pages before exploring further. # MiniMax M2.5 > Large MoE model with native reasoning and tool calling. Uses the MiniMax-specific append-think reasoning format.
Reasoning Tool calling Long context
## Setup To get started, sign into Baseten with Truss and then install the OpenAI SDK. **Sign in to Baseten** ```sh theme={"system"} uvx truss login --browser ``` **Install the OpenAI SDK** ```sh theme={"system"} uv pip install openai ``` [MiniMaxAI/MiniMax-M2.5](https://huggingface.co/MiniMaxAI/MiniMax-M2.5) is a 229B-parameter MoE model with up to 200K context. This preset serves MiniMax M2.5 on H100:4 with expert-parallel sharding and Runai Streamer weight loading, optimized for maximum batch throughput. H100 × 4 vLLM (latest build) 200K 64 ## Write the config Create and move into the project directory: ```sh theme={"system"} mkdir minimax-m2.5-throughput && cd minimax-m2.5-throughput ``` Then create a file named `config.yaml` and paste the following: ```yaml config.yaml theme={"system"} model_metadata: example_model_input: messages: - role: system content: "You are a helpful assistant." - role: user content: "What is the meaning of life?" stream: true model: MiniMaxAI/MiniMax-M2.5 max_tokens: 32768 temperature: 0.7 tags: - openai-compatible model_name: "model:minimax-m2.5 preset:throughput" base_image: image: vllm/vllm-openai:latest docker_server: start_command: > sh -c "SAFETENSORS_FAST_GPU=1 python3 -m vllm.entrypoints.openai.api_server --model /models/MiniMax-M2.5 --host 0.0.0.0 --port 8000 --served-model-name MiniMaxAI/MiniMax-M2.5 --tensor-parallel-size $(nvidia-smi -L | wc -l) --enable_expert_parallel --trust-remote-code --load-format runai_streamer --disable-log-stats --max-num-seqs 64 --max-num-batched-tokens 8192 --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think --enable-auto-tool-choice" readiness_endpoint: /health liveness_endpoint: /health predict_endpoint: /v1/chat/completions server_port: 8000 weights: - source: "hf://MiniMaxAI/MiniMax-M2.5@main" mount_location: "/models/MiniMax-M2.5" ignore_patterns: - "*.md" - "*.txt" resources: accelerator: H100:4 use_gpu: true runtime: predict_concurrency: 64 health_checks: restart_check_delay_seconds: 1800 restart_threshold_seconds: 1200 stop_traffic_threshold_seconds: 120 ``` ## Flags The `start_command` passes these flags to the engine. Each one controls a runtime or serving behavior: | Flag | Value | What it does | | --------------------------- | ------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------- | | `--model` | `/models/MiniMax-M2.5` | Path (or HF repo) the engine loads the model from. | | `--tensor-parallel-size` | `$(nvidia-smi` | Number of GPUs to shard the model across. | | `--enable_expert_parallel` | (no value) | Shard MoE expert weights across tensor-parallel ranks instead of replicating them, reducing per-GPU memory for large MoE models. | | `--trust-remote-code` | (no value) | Execute model-specific Python from the checkpoint (required for many Qwen, Phi, and custom architectures). | | `--load-format` | `runai_streamer` | Weight loading backend. **runai\_streamer:** Stream weights from object storage without materializing to disk. | | `--disable-log-stats` | (no value) | Suppress periodic engine stats logging. | | `--max-num-seqs` | `64` | Maximum number of concurrent sequences in the batch. | | `--max-num-batched-tokens` | `8192` | Maximum total tokens processed per scheduler step. | | `--tool-call-parser` | `minimax_m2` | Server-side parser that emits structured `tool_calls` on the response. **minimax\_m2:** MiniMax M2 tool format. | | `--reasoning-parser` | `minimax_m2_append_think` | Server-side parser that separates reasoning output into `reasoning_content`. **minimax\_m2\_append\_think:** MiniMax M2 append-think format. | | `--enable-auto-tool-choice` | (no value) | Let the model choose when to call tools without requiring `tool_choice: "required"`. | ## Deploy Push the config to Baseten: ```sh theme={"system"} uvx truss push ``` You should see output similar to: ```text theme={"system"} ✨ Model minimax-m2.5-throughput was successfully pushed ✨ 🪵 View logs for your deployment at https://app.baseten.co/models/abcd1234/logs/wxyz5678 ``` Your **model ID** is the string after `/models/` in the logs URL (`abcd1234` in the example). Use it wherever you see `{model_id}` in the next section. ## Call the model Your deployment serves an OpenAI-compatible API. Replace `{model_id}` with your model ID and make sure `BASETEN_API_KEY` is set. Now call your deployment to run inference: ```python main.py theme={"system"} import os from openai import OpenAI client = OpenAI( api_key=os.environ["BASETEN_API_KEY"], base_url="https://model-{model_id}.api.baseten.co/environments/production/sync/v1", ) response = client.chat.completions.create( model="MiniMaxAI/MiniMax-M2.5", messages=[ {"role": "user", "content": "What is machine learning?"} ], ) print(response.choices[0].message.content) ``` ```sh theme={"system"} curl -s https://model-{model_id}.api.baseten.co/environments/production/sync/v1/chat/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $BASETEN_API_KEY" \ -d '{ "model": "MiniMaxAI/MiniMax-M2.5", "messages": [ {"role": "user", "content": "What is machine learning?"} ] }' ``` The server parses the model's chain of thought into a separate `reasoning_content` field on the response. Read it alongside the final answer: ```python theme={"system"} response = client.chat.completions.create( model="MiniMaxAI/MiniMax-M2.5", messages=[ {"role": "user", "content": "How many r's in strawberry?"} ], ) print(response.choices[0].message.reasoning_content) # chain of thought print(response.choices[0].message.content) # final answer ``` To let the model call tools, pass a `tools` array. The server returns structured `tool_calls` on the response: ```python theme={"system"} tools = [{ "type": "function", "function": { "name": "get_weather", "parameters": { "type": "object", "properties": {"location": {"type": "string"}}, "required": ["location"], }, }, }] response = client.chat.completions.create( model="MiniMaxAI/MiniMax-M2.5", messages=[ {"role": "user", "content": "What's the weather in Paris?"} ], tools=tools, ) print(response.choices[0].message.tool_calls) ```