apps/infsh/mistral-small-3-2-24b-it-2506

mistral-small-3-2-24b-it-2506

Follows precise instructions, excels at function/tool calling, and can process both text and images for tasks like document understanding and content generation.

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/mistral-small-3-2-24b-it-2506

# run

$belt app run infsh/mistral-small-3-2-24b-it-2506

api reference

about

follows precise instructions, excels at function/tool calling, and can process both text and images for tasks like document understanding and content generation.

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/mistral-small-3-2-24b-it-2506",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/mistral-small-3-2-24b-it-2506",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/mistral-small-3-2-24b-it-2506",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/mistral-small-3-2-24b-it-2506",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

contextarray

the context to use for the model

default: []example: [{"content":[{"text":"What is the capital of France?","type":"text"}],"role":"user"},{"content":[{"text":"The capital of France is Paris.","type":"text"}],"role":"assistant"}]

context_sizeinteger

context size

default: 4096

imagesarray

the images to use for the model

reasoningstring

the reasoning input of the message

reasoning_effortstring

enable step-by-step reasoning

default: "none"

options:"low""medium""high""none"

reasoning_max_tokensinteger

the maximum number of tokens to use for reasoning

rolestring

the role of the input text

default: "user"

options:"user""assistant""system""tool"

system_promptstring

the system prompt to use for the model

default: "A user will ask you to solve a task. You should first draft your thinking process (inner monologue) until you have derived the final answer. Afterwards, write a self-contained summary of your thoughts (i.e. your summary should be succinct but contain all the critical steps you needed to reach the conclusion). You should use Markdown and Latex to format your response. Write both your thoughts and summary in the same language as the task posed by the user.\n\nYour thinking process must follow the template below:\n<think>\nYour thoughts or/and draft, like working through an exercise on scratch paper. Be as casual and as long as you want until you are confident to generate a correct answer.\n</think>\n\nHere, provide a concise summary that reflects your reasoning and presents a clear final answer to the user.\n\nProblem:"

temperaturenumber

temperature

default: 0.7min:0max:1

textstring*

the input text to use for the model

example: "write a haiku about artificial general intelligence"

tool_call_idstring

the tool call id for tool role messages

toolsarray

tool definitions for function calling

top_pnumber

top p

default: 0.95min:0max:1

output

responsestring*

the generated text response

usageobject

token usage statistics

reasoningstring

the reasoning output of the model

tool_callsarray

tool calls for function calling

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