Inference Logoinference shell
apps/infsh/decision-2-0-sol-2b

Decision 2.0 Sol 2B

The 2B Decision 2.0 model from vLLM Semantic Router. Send a state and typed questions (choice, score, yes/no); get a probability for every answer in one forward pass, with no text generation. 1.88B parameters, 16,384-token input, about 7.2 ms per question.

run in browser →run via API
run with your agent
# install belt
$curl -fsSL https://cli.inference.sh | sh
# view schema & details
$belt app get infsh/decision-2-0-sol-2b
# run
$belt app run infsh/decision-2-0-sol-2b

api reference

about

the 2b decision 2.0 model from vllm semantic router. send a state and typed questions (choice, score, yes/no); get a probability for every answer in one forward pass, with no text generation. 1.88b parameters, 16,384-token input, about 7.2 ms per question.

1. calling the api

install the client

the client provides a convenient way to interact with the api.

bash
1pip install inferencesh

setup your api key

set INFERENCE_API_KEY as an environment variable. get your key from settings → api keys.

bash
1export INFERENCE_API_KEY="inf_your_key"

run and get result

submit a request and wait for the final result. best for batch processing or when you don't need progress updates.

python
1from inferencesh import inference23client = inference()456result = client.run({7        "app": "infsh/decision-2-0-sol-2b",8        "input": {}9    })1011print(result["output"])

stream live updates

get real-time progress updates as the task runs. ideal for showing progress bars, partial results, or long-running tasks.

python
1from inferencesh import inference23client = inference()456# stream=True yields updates as they arrive7for update in client.run({8        "app": "infsh/decision-2-0-sol-2b",9        "input": {}10    }, stream=True):11    if update.get("progress"):12        print(f"progress: {update['progress']}%")13    if update.get("output"):14        print(f"output: {update['output']}")

2. authentication

the api uses api keys for authentication. see the authentication docs for detailed setup instructions.

3. files

file inputs are automatically handled by the sdk. you can pass local paths, urls, or base64 data.

automatic upload

the python sdk automatically detects local file paths and uploads them. urls are passed through as-is.

python
1# local file paths are automatically uploaded2result = client.run({3    "app": "infsh/decision-2-0-sol-2b",4    "input": {5        "image": "/path/to/local/image.png",  # detected & uploaded6        "audio": "https://example.com/audio.mp3",  # url passed through7    }8})

manual upload

you can also upload files manually and use the returned url.

python
1# upload and get a hosted URL2file = client.files.upload("/path/to/file.png")3print(file.uri)  # https://cloud.inference.sh/...

4. webhooks

get notified when a task completes by providing a webhook url. when the task reaches a terminal state (completed, failed, or cancelled), a POST request is sent to your url with the task result.

python
1result = client.run({2    "app": "infsh/decision-2-0-sol-2b",3    "input": {},4    "webhook": "https://your-server.com/webhook"5}, wait=False)

webhook payload

your endpoint receives a JSON POST with the task result:

json
1{2  "id": "task_abc123",3  "status": 9,4  "output": { ... },5  "error": "",6  "session_id": null,7  "created_at": "2024-01-15T10:30:00Z",8  "updated_at": "2024-01-15T10:30:05Z"9}
idstring— task id
statusnumber— terminal status (9=completed, 10=failed, 11=cancelled)
outputobject— task output (when completed)
errorstring— error message (when failed)
session_idstring— session id (if using sessions)
created_atstring— iso timestamp
updated_atstring— iso timestamp

5. schema

input

stateany*

the content to evaluate: a string, or a json object / array of related context (messages, records, a policy). text only. every question sees the same state. input over the model's token limit is rejected, never truncated.

example: "The order arrived damaged yesterday. The customer has a receipt and asks for a replacement today."
choicesarray

choice questions: pick one option from a set.

scoresarray

score questions: place the state on ordered levels.

noulsarray

noul questions: probability that the answer is yes.

output

choicesobject

choice answers by question id.

input_tokensinteger

input tokens, summed over the questions.

modelstring*

the model that answered, e.g. `decision-2.0-sol-2b`.

noulsobject

noul answers by question id.

scoresobject

score answers by question id.

ready to run Decision 2.0 Sol 2B?

try in browserbrowse all tools

we use cookies

we use cookies to ensure you get the best experience on our website. for more information on how we use cookies, please see our cookie policy.

by clicking "accept", you agree to our use of cookies.
learn more.