> ## 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. # Laguna > Poolside's Laguna M.1 is a Mixture-of-Experts reasoning model tuned for agentic coding and extended reasoning, served from an FP8 checkpoint.
Agentic 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 ``` [poolside/Laguna-M.1-FP8](https://huggingface.co/poolside/Laguna-M.1-FP8) is a MoE model with up to 256K context. This preset serves Laguna M.1 on H100:4 with FP8 weights, optimized for low time-to-first-token on interactive reasoning and coding workloads. H100 × 4 vLLM 0.21.0 256K 64 ## Write the config Create and move into the project directory: ```sh theme={"system"} mkdir laguna-m.1-latency && cd laguna-m.1-latency ``` Then create a file named `config.yaml` and paste the following: ```yaml config.yaml theme={"system"} model_name: "model:laguna-m.1 preset:latency" model_metadata: description: >- Laguna M.1 FP8 MoE reasoning model from Poolside, served with vLLM (H100 TP=4), OpenAI-compatible chat with tool calling and extended reasoning support. Latency-optimized: low max-num-seqs to minimize head-of-line blocking from long thinking traces. repo_id: poolside/Laguna-M.1-FP8 trust_remote_code: true tags: - openai-compatible - vllm - moe - reasoning - agentic-coding - fp8 example_model_input: model: poolside/laguna-m.1 messages: - role: user content: "Write a Python retry wrapper with exponential backoff." stream: true temperature: 1.0 top_k: 20 # --------------------------------------------------------------------------- # Base image — vLLM with Laguna support (requires vLLM >= 0.21.0) # --------------------------------------------------------------------------- base_image: image: vllm/vllm-openai:v0.21.0 python_executable_path: /usr/bin/python3 # --------------------------------------------------------------------------- # Weights — FP8 quantized checkpoint (~225 GB, fits in 4× H100 / 320 GB) # Quantization is detected automatically from the checkpoint's # quantization_config — no extra vLLM flags needed. # --------------------------------------------------------------------------- weights: - source: "hf://poolside/Laguna-M.1-FP8" mount_location: "/models/laguna-m1" environment_variables: VLLM_LOGGING_LEVEL: WARNING VLLM_ENGINE_READY_TIMEOUT_S: "3600" # --------------------------------------------------------------------------- # Docker server — vLLM OpenAI-compatible endpoint # --------------------------------------------------------------------------- docker_server: start_command: > vllm serve /models/laguna-m1 --served-model-name poolside/laguna-m.1 --host 0.0.0.0 --port 8000 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice --default-chat-template-kwargs '{"enable_thinking": true}' --tensor-parallel-size 4 --max-model-len 262144 --max-num-seqs 64 --gpu-memory-utilization 0.95 --trust-remote-code readiness_endpoint: /health liveness_endpoint: /health predict_endpoint: /v1/chat/completions server_port: 8000 # --------------------------------------------------------------------------- # Resources # FP8 ~225 GB → 4× H100 (320 GB total VRAM) with comfortable headroom # --------------------------------------------------------------------------- resources: accelerator: H100:4 cpu: "8" memory: 32Gi use_gpu: true # --------------------------------------------------------------------------- # Runtime # --------------------------------------------------------------------------- runtime: predict_concurrency: 64 health_checks: restart_check_delay_seconds: 1800 restart_threshold_seconds: 600 stop_traffic_threshold_seconds: 180 ``` ## Flags The `start_command` passes these flags to the engine. Each one controls a runtime or serving behavior: | Flag | Value | What it does | | -------------------------------- | --------------------------- | ----------------------------------------------------------------------------------------------------------------- | | `--tool-call-parser` | `poolside_v1` | Server-side parser that emits structured `tool_calls` on the response. | | `--reasoning-parser` | `poolside_v1` | Server-side parser that separates reasoning output into `reasoning_content`. | | `--enable-auto-tool-choice` | (no value) | Let the model choose when to call tools without requiring `tool_choice: "required"`. | | `--default-chat-template-kwargs` | `{"enable_thinking": true}` | Default keyword arguments applied to the chat template, used to set behaviors like enabling reasoning by default. | | `--tensor-parallel-size` | `4` | Number of GPUs to shard the model across. | | `--max-model-len` | `262144` | Maximum context length (tokens) the server accepts per request. | | `--max-num-seqs` | `64` | Maximum number of concurrent sequences in the batch. | | `--gpu-memory-utilization` | `0.95` | Fraction of GPU memory vLLM may use for weights and KV cache. | | `--trust-remote-code` | (no value) | Execute model-specific Python from the checkpoint (required for many Qwen, Phi, and custom architectures). | ## Deploy Push the config to Baseten: ```sh theme={"system"} uvx truss push ``` You should see output similar to: ```output theme={"system"} ✨ Model laguna-m.1-latency was successfully pushed ✨ Model ID: abc1d2ef Deployment ID: xyz123 Endpoint: model-abc1d2ef.api.baseten.co Logs: https://app.baseten.co/models/abc1d2ef/logs/xyz123 ``` Your **model ID** is printed in the `truss push` output (`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="poolside/laguna-m.1", 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": "poolside/laguna-m.1", "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="poolside/laguna-m.1", 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="poolside/laguna-m.1", messages=[ {"role": "user", "content": "What's the weather in Paris?"} ], tools=tools, ) print(response.choices[0].message.tool_calls) ```