> ## 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.
Reasoning Tool calling Long context
Large MoE model with native reasoning and tool calling. Uses the MiniMax-specific append-think reasoning format. ## Setup Install the Baseten CLI and sign in, then install the OpenAI SDK. **Install and sign in to Baseten** ```bash Terminal theme={"system"} brew tap basetenlabs/baseten brew trust --formula basetenlabs/baseten/baseten brew install baseten ``` Download and extract the binary, then move `baseten.exe` to a directory on your `PATH`: ```powershell Terminal theme={"system"} Invoke-WebRequest ` https://github.com/basetenlabs/baseten-cli/releases/download/v1.0.0/baseten_1.0.0_windows_amd64.zip ` -OutFile baseten.zip; Expand-Archive -Force baseten.zip . ``` For other platforms or a specific version, see the [Baseten CLI install reference](/reference/cli/baseten/overview#install). ```sh theme={"system"} baseten auth login ``` **Install the OpenAI SDK** ```sh theme={"system"} uv pip install openai ``` To install without Homebrew, see the [Baseten CLI install reference](/reference/cli/baseten/overview#install). 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 (0.22.0-cu129 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_name: "model:minimax-m2.5 preset:throughput" model_metadata: description: >- MiniMax-M2.5 Mixture-of-Experts (Run:AI streamer loading), throughput on H100 × 4 with MiniMax parsers. repo_id: MiniMaxAI/MiniMax-M2.5 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 base_image: image: vllm/vllm-openai:v0.22.0-cu129 weights: - source: "hf://MiniMaxAI/MiniMax-M2.5@main" mount_location: "/app/checkpoint/model" auth_secret_name: "hf_access_token" ignore_patterns: - "*.md" - "*.txt" secrets: hf_access_token: null environment_variables: VLLM_LOGGING_LEVEL: WARNING VLLM_ENGINE_READY_TIMEOUT_S: "3600" docker_server: start_command: >- sh -c 'GPU_COUNT=$(nvidia-smi --list-gpus | wc -l) && SAFETENSORS_FAST_GPU=1 vllm serve /app/checkpoint/model --host 0.0.0.0 --port 8000 --served-model-name MiniMaxAI/MiniMax-M2.5 --tensor-parallel-size $GPU_COUNT --enable-expert-parallel --trust-remote-code --load-format runai_streamer --disable-log-stats --max-num-seqs 64 --max-num-batched-tokens 8192 --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think --enable-auto-tool-choice --enable-prefix-caching' readiness_endpoint: /health liveness_endpoint: /health predict_endpoint: /v1/chat/completions server_port: 8000 resources: accelerator: H100:4 use_gpu: true runtime: predict_concurrency: 64 health_checks: startup_threshold_seconds: 1800 restart_threshold_seconds: 1200 stop_traffic_threshold_seconds: 120 ``` ## Flags The `start_command` passes these flags to the engine. Each one controls a runtime or serving behavior: | Flag | Value | What it does | | --------------------------- | ------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------- | | `--tensor-parallel-size` | `$GPU_COUNT` | 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"`. | | `--enable-prefix-caching` | (no value) | Reuse KV cache across requests that share a prefix. | ## Deploy Push the config to Baseten with the Baseten CLI, or with the Truss CLI if you prefer it: ```sh Baseten CLI theme={"system"} baseten model push ``` ```sh Truss CLI theme={"system"} uvx truss push ``` You should see output similar to: ```output theme={"system"} Pushing model "minimax-m2.5-throughput"... Uploading model... Uploaded model in 0s ✨ Model minimax-m2.5-throughput was successfully pushed ✨ Model: minimax-m2.5-throughput (abc1d2ef) Deployment: xyz123 Environment: production 🪵 View logs: deployment: baseten model deployment logs --model-id abc1d2ef --deployment-id xyz123 environment: baseten model environment logs --model-id abc1d2ef --environment production (once deployed) app: https://app.baseten.co/models/abc1d2ef/logs/xyz123 🚀 Invoke your model: URL: https://model-abc1d2ef.api.baseten.co/deployment/xyz123/predict CLI: baseten model predict --model-id abc1d2ef ``` `baseten model push` prints your **model ID** (`abc1d2ef` in the example). The examples below use it wherever you see `{model_id}`, and read your API key from the `BASETEN_API_KEY` environment variable. ## Call the model Your deployment serves an OpenAI-compatible API. 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) ``` ## Next steps Endpoint anatomy, authentication, and sync versus async inference Scale replicas with traffic, including scale to zero