> ## 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. # Holo 3.1 > H Company's Holo 3.1 is a 35B-parameter MoE vision-language model with 3B active parameters per token, built on the Qwen3.6-35B-A3B base. It accepts image input and returns reasoning output and native tool calls.
Agentic Multimodal (image) 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 ``` [Hcompany/Holo-3.1-35B-A3B-FP8](https://huggingface.co/Hcompany/Holo-3.1-35B-A3B-FP8) is a 35B-parameter MoE model (3B active per token) with up to 256K context. This preset serves Holo 3.1 35B-A3B on H100 GPUs with FP8 weights, optimized for high-concurrency throughput on agent and vision workloads. H100 vLLM (0.20.2-cu129 build) 256K 1000 ## Write the config Create and move into the project directory: ```sh theme={"system"} mkdir holo-3.1-35b-a3b-throughput && cd holo-3.1-35b-a3b-throughput ``` Then create a file named `config.yaml` and paste the following: ```yaml config.yaml theme={"system"} model_name: "model:holo-3.1-35b-a3b preset:throughput" model_metadata: description: >- Holo-3.1-35B-A3B (FP8), H Company's computer-use / GUI-agent vision-language model built on the Qwen3.6-35B-A3B MoE base. OpenAI-compatible multimodal chat with image input and native function calling, served via vLLM. repo_id: Hcompany/Holo-3.1-35B-A3B-FP8 example_model_input: model: Hcompany/Holo-3.1-35B-A3B-FP8 messages: - role: user content: - type: text text: "Describe this image in one sentence." - type: image_url image_url: url: "https://picsum.photos/id/237/200/300" stream: true max_tokens: 512 temperature: 1.0 tags: - openai-compatible - vllm - holo3.1 - fp8 - h100 - multimodal base_image: image: vllm/vllm-openai:v0.20.2-cu129 weights: - source: "hf://Hcompany/Holo-3.1-35B-A3B-FP8@main" mount_location: "/app/checkpoint/model" auth_secret_name: "hf_access_token" build_commands: [] environment_variables: PYTORCH_ALLOC_CONF: "expandable_segments:True" VLLM_LOGGING_LEVEL: WARNING VLLM_ENGINE_READY_TIMEOUT_S: "3600" docker_server: start_command: >- sh -c "GPU_COUNT=$(nvidia-smi --list-gpus | wc -l) && vllm serve /app/checkpoint/model --tensor-parallel-size $GPU_COUNT --served-model-name Hcompany/Holo-3.1-35B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --max-model-len 262144 --max-num-batched-tokens 32768 --dtype auto --enable-chunked-prefill --enable-prefix-caching --max-num-seqs 512 --limit-mm-per-prompt.image 2 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --trust-remote-code" readiness_endpoint: /health liveness_endpoint: /health predict_endpoint: /v1/chat/completions server_port: 8000 runtime: predict_concurrency: 1000 health_checks: restart_check_delay_seconds: 1500 restart_threshold_seconds: 1500 stop_traffic_threshold_seconds: 120 resources: accelerator: H100 use_gpu: true secrets: hf_access_token: null ``` ## Flags The `start_command` passes these flags to the engine. Each one controls a runtime or serving behavior: | Flag | Value | What it does | | ----------------------------- | ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- | | `--tensor-parallel-size` | `$GPU_COUNT` | Number of GPUs to shard the model across. | | `--gpu-memory-utilization` | `0.95` | Fraction of GPU memory vLLM may use for weights and KV cache. | | `--max-model-len` | `262144` | Maximum context length (tokens) the server accepts per request. | | `--max-num-batched-tokens` | `32768` | Maximum total tokens processed per scheduler step. | | `--dtype` | `auto` | Weight precision loaded at runtime. **auto:** Match the model's checkpoint dtype (default). | | `--enable-chunked-prefill` | (no value) | Process long prompts in chunks so decode requests keep running. | | `--enable-prefix-caching` | (no value) | Reuse KV cache across requests that share a prefix. | | `--max-num-seqs` | `512` | Maximum number of concurrent sequences in the batch. | | `--limit-mm-per-prompt.image` | `2` | Maximum number of image inputs per prompt. | | `--reasoning-parser` | `qwen3` | Server-side parser that separates reasoning output into `reasoning_content`. **qwen3:** Qwen3-family thinking format (used by Qwen3, Qwen3.5, and Qwen3.6). | | `--enable-auto-tool-choice` | (no value) | Let the model choose when to call tools without requiring `tool_choice: "required"`. | | `--tool-call-parser` | `qwen3_coder` | Server-side parser that emits structured `tool_calls` on the response. **qwen3\_coder:** Qwen3-Coder tool format. | | `--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 holo-3.1-35b-a3b-throughput 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="Hcompany/Holo-3.1-35B-A3B-FP8", 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": "Hcompany/Holo-3.1-35B-A3B-FP8", "messages": [ {"role": "user", "content": "What is machine learning?"} ] }' ``` To access the model's chain of thought, enable thinking mode. The server parses the reasoning output into a separate `reasoning_content` field on the response: ```python theme={"system"} response = client.chat.completions.create( model="Hcompany/Holo-3.1-35B-A3B-FP8", messages=[ {"role": "user", "content": "How many r's in strawberry?"} ], extra_body={"chat_template_kwargs": {"enable_thinking": True}}, ) 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="Hcompany/Holo-3.1-35B-A3B-FP8", messages=[ {"role": "user", "content": "What's the weather in Paris?"} ], tools=tools, ) print(response.choices[0].message.tool_calls) ```