Ornith 1.5

Reasoning Tool calling Multimodal (image)Long context Agentic

Ornith 1.5 35B-A3B is an FP8 mixture-of-experts reasoning model for coding and agentic tasks. It activates about 3B parameters per token and serves OpenAI-compatible multimodal chat, reasoning content, and tool calls with vLLM.

Setup

Sign in to Baseten

uvx truss login --browser

Install the OpenAI SDK

uv pip install openai

This preset serves Ornith 1.5 35B-A3B on H100:2 with FP8 weights and tensor parallel size 2, optimized for low-latency interactive chat, reasoning, and agent workflows.

Hardware

Engine

vLLM 0.19.1

Context

256K

Concurrency

Write the config

Create and move into the project directory:

mkdir ornith-1.5-35b-a3b-latency && cd ornith-1.5-35b-a3b-latency

Then create a file named config.yaml and paste the following:

config.yaml

model_name: "model:ornith-1.5-35b-a3b preset:latency"

model_metadata:
  description: >-
    Ornith 1.5 35B-A3B is an FP8 mixture-of-experts reasoning model for coding
    and agentic tasks. It activates about 3B parameters per token and serves
    OpenAI-compatible multimodal chat, reasoning content, and tool calls with vLLM.
  repo_id: ornith-ai/Ornith-1.5-35B-A3B-FP8
  tags:
    - openai-compatible
    - multimodal
    - reasoning
    - tool-use
  example_model_input:
    model: Ornith-1.5-35B-A3B
    messages:
      - role: user
        content: "Write a one-line Python lambda that squares a number."
    max_tokens: 1024
    temperature: 0.6
    top_p: 0.95

base_image:
  image: vllm/vllm-openai:v0.19.1

weights:
  - source: "hf://ornith-ai/Ornith-1.5-35B-A3B-FP8@0e048080ccd0ccf4296bfea5638036c196dccc0c"
    mount_location: "/app/checkpoint/model"
    auth_secret_name: "hf_access_token"

secrets:
  hf_access_token: null

environment_variables:
  VLLM_LOGGING_LEVEL: WARNING
  VLLM_ENGINE_READY_TIMEOUT_S: "3600"

docker_server:
  start_command: >-
    vllm serve /app/checkpoint/model
    --served-model-name Ornith-1.5-35B-A3B
    --host 0.0.0.0
    --port 8000
    --tensor-parallel-size 2
    --max-model-len 262144
    --gpu-memory-utilization 0.90
    --enable-prefix-caching
    --enable-auto-tool-choice
    --tool-call-parser qwen3_xml
    --reasoning-parser qwen3
    --trust-remote-code
    --limit-mm-per-prompt.image 1
  readiness_endpoint: /health
  liveness_endpoint: /health
  predict_endpoint: /v1/chat/completions
  server_port: 8000

resources:
  instance_type: H100:2

runtime:
  predict_concurrency: 32
  health_checks:
    restart_check_delay_seconds: 1800
    restart_threshold_seconds: 1200
    stop_traffic_threshold_seconds: 120

This preset deploys the FP8 checkpoint of Ornith 1.5 35B-A3B through vLLM on two H100 GPUs with tensor parallelism. It enables prefix caching and preserves the checkpoint’s native 256K context window. The deployment exposes an OpenAI-compatible chat completions endpoint with reasoning, tool calling, and one image per prompt.

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`	`2`	Number of GPUs to shard the model across.
`--max-model-len`	`262144`	Maximum context length (tokens) the server accepts per request.
`--gpu-memory-utilization`	`0.90`	Fraction of GPU memory vLLM may use for weights and KV cache.
`--enable-prefix-caching`	(no value)	Reuse KV cache across requests that share a prefix.
`--enable-auto-tool-choice`	(no value)	Let the model choose when to call tools without requiring `tool_choice: "required"`.
`--tool-call-parser`	`qwen3_xml`	Server-side parser that emits structured `tool_calls` on the response.
`--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).
`--trust-remote-code`	(no value)	Execute model-specific Python from the checkpoint (required for many Qwen, Phi, and custom architectures).
`--limit-mm-per-prompt.image`	`1`	Maximum number of image inputs per prompt.

Deploy

Push the config to Baseten:

uvx truss push

You should see output similar to:

✨ Model ornith-1.5-35b-a3b-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

truss 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
cURL

main.py

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="Ornith-1.5-35B-A3B",
    messages=[
        {"role": "user", "content": "What is machine learning?"}
    ],
)

print(response.choices[0].message.content)

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": "Ornith-1.5-35B-A3B",
    "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:

response = client.chat.completions.create(
    model="Ornith-1.5-35B-A3B",
    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:

tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "parameters": {
            "type": "object",
            "properties": {"location": {"type": "string"}},
            "required": ["location"],
        },
    },
}]

response = client.chat.completions.create(
    model="Ornith-1.5-35B-A3B",
    messages=[
        {"role": "user", "content": "What's the weather in Paris?"}
    ],
    tools=tools,
)
print(response.choices[0].message.tool_calls)

Examples

Models

Engines

Custom Docker servers

Custom Python models

Chains

Setup

Hardware

Engine

Context

Concurrency

Write the config

Flags

Deploy

Call the model

Next steps

Call your model

Autoscaling

​Setup

Hardware

Engine

Context

Concurrency

​Write the config

​Flags

​Deploy

​Call the model

​Next steps

Call your model

Autoscaling

Setup

Write the config

Flags

Deploy

Call the model

Next steps