> ## Documentation Index > Fetch the complete documentation index at: https://docs.inferless.com/llms.txt > Use this file to discover all available pages before exploring further. # Deploy the Mistral-Small-3.1-24B-Instruct-2503 using Inferless > Mistral-Small-3.1-24B-Instruct-2503 is a 24-billion-parameter language model fine-tuned for instruction-following tasks and equipped with state-of-the-art vision understanding. Optimized for efficient inference and function calling, it is ideal for fast-response conversational agents, local inference on sensitive data, and advanced multi-modal applications. ## Introduction [Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503) builds upon the base model by incorporating both text and vision capabilities model designed for performance across both text and vision tasks. With 24B parameters, this instruction-finetuned model builds upon the Mistral Small 3.1 base, offering enhanced capabilities such as state-of-the-art vision understanding and support for long context lengths up to 128k tokens without sacrificing text processing quality. ## Defining Dependencies We are using the [transformers](https://github.com/huggingface/transformers) to serve the model on a single A100. ## Our Observations We have deployed the model on an A100 GPU(80GB). Here are our observations: | Library | Inference Time | Cold Start Time | Tokens/Sec | Output Tokens Length | | ------------ | -------------- | --------------- | ---------- | -------------------- | | transformers | 2.06 sec | 17.69 sec | 21.99 | 256 | Note: The inference time and cold start time are average values. ## Defining Dependencies We are using the [transformers](https://github.com/huggingface/transformers) to serve the model on a single A100 (80GB). ## Constructing the GitHub/GitLab Template Now quickly construct the GitHub/GitLab template, this process is mandatory and make sure you don't add any file named `model.py`. ``` mistral-small-3.1-24b-instruct/ ├── app.py ├── inferless-runtime-config.yaml └── inferless.yaml ``` You can also add other files to this directory. ## Create the Input Schema with Pydantic Using the `inferless` Python client and Pydantic, you can define structured schemas directly in your code for input and output, eliminating the need for external file. ### Input Schema When defining an input schema with Pydantic, you need to annotate your class attributes with the appropriate types, such as `str`, `float`, `int`, etc. These type annotations specifys what type of data each field should contain. The `default` value serves as the example input for testing with the `infer` function. ```python theme={null} @inferless.request class RequestObjects(BaseModel): prompt: str = Field(default="Give me 5 non-formal ways to say 'See you later' in French.") system_prompt: Optional[str] = "You are a conversational agent that always answers straight to the point." temperature: Optional[float] = 0.7 top_p: Optional[float] = 0.1 repetition_penalty: Optional[float] = 1.18 top_k: Optional[int] = 40 max_tokens: Optional[int] = 100 do_sample: Optional[bool] = False ``` ### Output Schema The `@inferless.response` decorator helps you define structured output schemas. ```python theme={null} @inferless.response class ResponseObjects(BaseModel): generated_text: str = Field(default="Test output") ``` ### Usage in the `infer` Function Once you have annotated the objects you can expect the infer function to receive `RequestObjects` as input, and returns a `ResponseObjects` instance as output, ensuring the results adhere to a defined structure. ```python theme={null} class InferlessPythonModel: def infer(self, request: RequestObjects) -> ResponseObjects: return ResponseObjects(generated_text=generated_text) ``` ## Create the class for inference In the [app.py](https://github.com/inferless/mistral-small-3.1-24b-instruct/blob/main/app.py) we will define the class and import all the required functions 1. `def initialize`: In this function, you will initialize your model and define any `variable` that you want to use during inference. 2. `def infer`: This function gets called for every request that you send. Here you can define all the steps that are required for the inference. 3. `def finalize`: This function cleans up all the allocated memory. ```python theme={null} import os os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1" from huggingface_hub import snapshot_download from pydantic import BaseModel, Field from typing import Optional import inferless import torch from transformers import AutoTokenizer, Mistral3ForConditionalGeneration, BitsAndBytesConfig @inferless.request class RequestObjects(BaseModel): prompt: str = Field(default="Give me 5 non-formal ways to say 'See you later' in French.") system_prompt: Optional[str] = "You are a conversational agent that always answers straight to the point." temperature: Optional[float] = 0.7 top_p: Optional[float] = 0.1 repetition_penalty: Optional[float] = 1.18 top_k: Optional[int] = 40 max_tokens: Optional[int] = 100 do_sample: Optional[bool] = False @inferless.response class ResponseObjects(BaseModel): generated_text: str = Field(default="Test output") class InferlessPythonModel: def initialize(self): model_id = "mistralai/Mistral-Small-3.1-24B-Instruct-2503" snapshot_download(repo_id=model_id, allow_patterns=["*.safetensors"]) quantization_config = BitsAndBytesConfig(load_in_4bit=True) self.tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) self.tokenizer.chat_template = "{%- set today = strftime_now(\"%Y-%m-%d\") %}\n{%- set default_system_message = \"You are Mistral Small 3, a Large Language Model (LLM) created by Mistral AI, a French startup headquartered in Paris.\\nYour knowledge base was last updated on 2023-10-01. The current date is \" + today + \".\\n\\nWhen you're not sure about some information, you say that you don't have the information and don't make up anything.\\nIf the user's question is not clear, ambiguous, or does not provide enough context for you to accurately answer the question, you do not try to answer it right away and you rather ask the user to clarify their request (e.g. \\\"What are some good restaurants around me?\\\" => \\\"Where are you?\\\" or \\\"When is the next flight to Tokyo\\\" => \\\"Where do you travel from?\\\")\" %}\n\n{{- bos_token }}\n\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content'] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set system_message = default_system_message %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{{- '[SYSTEM_PROMPT]' + system_message + '[/SYSTEM_PROMPT]' }}\n\n{%- for message in loop_messages %}\n {%- if message['role'] == 'user' %}\n\t {%- if message['content'] is string %}\n {{- '[INST]' + message['content'] + '[/INST]' }}\n\t {%- else %}\n\t\t {{- '[INST]' }}\n\t\t {%- for block in message['content'] %}\n\t\t\t {%- if block['type'] == 'text' %}\n\t\t\t\t {{- block['text'] }}\n\t\t\t {%- elif block['type'] == 'image' or block['type'] == 'image_url' %}\n\t\t\t\t {{- '[IMG]' }}\n\t\t\t\t{%- else %}\n\t\t\t\t {{- raise_exception('Only text and image blocks are supported in message content!') }}\n\t\t\t\t{%- endif %}\n\t\t\t{%- endfor %}\n\t\t {{- '[/INST]' }}\n\t\t{%- endif %}\n {%- elif message['role'] == 'system' %}\n {{- '[SYSTEM_PROMPT]' + message['content'] + '[/SYSTEM_PROMPT]' }}\n {%- elif message['role'] == 'assistant' %}\n {{- message['content'] + eos_token }}\n {%- else %}\n {{- raise_exception('Only user, system and assistant roles are supported!') }}\n {%- endif %}\n{%- endfor %}" self.model = Mistral3ForConditionalGeneration.from_pretrained( model_id, trust_remote_code=True, quantization_config=quantization_config, device_map="cuda", ) def infer(self, request: RequestObjects) -> ResponseObjects: system_prompt = "You are a conversational agent that always answers straight to the point." if request.system_prompt is not None: system_prompt = request.system_prompt messages = [ { "role": "system", "content": system_prompt }, { "role": "user", "content": request.prompt }, ] tokenized_chat = self.tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda") with torch.no_grad(): generation = self.model.generate( tokenized_chat, max_new_tokens=request.max_tokens, temperature=request.temperature, top_p=request.top_p, top_k=request.top_k, do_sample=request.do_sample, repetition_penalty=request.repetition_penalty, ) generated_text = self.tokenizer.decode(generation[0], skip_special_tokens=True) return ResponseObjects(generated_text=generated_text) def finalize(self): self.model = None ``` ## Creating the Custom Runtime This is a mandatory step where we allow the users to upload their custom runtime through [inferless-runtime-config.yaml](https://github.com/inferless/mistral-small-3.1-24b-instruct/blob/main/inferless-runtime-config.yaml). ```python theme={null} build: cuda_version: "12.1.1" python_packages: - "git+https://github.com/huggingface/transformers@v4.49.0-Mistral-3" - "bitsandbytes==0.45.3" - "hf_transfer==0.1.9" - "inferless==0.2.14" - "pydantic==2.10.6" - "accelerate==1.5.2" ``` ## Test your model with Remote Run You can use the `inferless remote-run`([installation guide here](https://docs.inferless.com/model-import/cli-import#cli-import)) command to test your model or any custom Python script in a remote GPU environment directly from your local machine. Make sure that you use `Python3.10` for seamless experience. ### Step 1: Add the Decorators and local entry point To enable **Remote Run**, simply do the following: 1. Import the `inferless` library and initialize `Cls(gpu="A100")`. The available GPU options are `T4`, `A10` and `A100`. 2. Decorated the `initialize` and `infer` functions with `@app.load` and `@app.infer` respectively. 3. Create the Local Entry Point by decorating a function (for example, `my_local_entry`) with `@inferless.local_entry_point`. Within this function, instantiate your model class, convert any incoming parameters into a `RequestObjects` object, and invoke the model's `infer` method. ```python theme={null} import os os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1" from huggingface_hub import snapshot_download from pydantic import BaseModel, Field from typing import Optional import inferless import torch from transformers import AutoTokenizer, Mistral3ForConditionalGeneration, BitsAndBytesConfig @inferless.request class RequestObjects(BaseModel): prompt: str = Field(default="Give me 5 non-formal ways to say 'See you later' in French.") system_prompt: Optional[str] = "You are a conversational agent that always answers straight to the point." temperature: Optional[float] = 0.7 top_p: Optional[float] = 0.1 repetition_penalty: Optional[float] = 1.18 top_k: Optional[int] = 40 max_tokens: Optional[int] = 100 do_sample: Optional[bool] = False @inferless.response class ResponseObjects(BaseModel): generated_text: str = Field(default="Test output") app = inferless.Cls(gpu="A100") class InferlessPythonModel: @app.load def initialize(self): model_id = "mistralai/Mistral-Small-3.1-24B-Instruct-2503" snapshot_download(repo_id=model_id, allow_patterns=["*.safetensors"]) quantization_config = BitsAndBytesConfig(load_in_4bit=True) self.tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) self.tokenizer.chat_template = "{%- set today = strftime_now(\"%Y-%m-%d\") %}\n{%- set default_system_message = \"You are Mistral Small 3, a Large Language Model (LLM) created by Mistral AI, a French startup headquartered in Paris.\\nYour knowledge base was last updated on 2023-10-01. The current date is \" + today + \".\\n\\nWhen you're not sure about some information, you say that you don't have the information and don't make up anything.\\nIf the user's question is not clear, ambiguous, or does not provide enough context for you to accurately answer the question, you do not try to answer it right away and you rather ask the user to clarify their request (e.g. \\\"What are some good restaurants around me?\\\" => \\\"Where are you?\\\" or \\\"When is the next flight to Tokyo\\\" => \\\"Where do you travel from?\\\")\" %}\n\n{{- bos_token }}\n\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content'] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set system_message = default_system_message %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{{- '[SYSTEM_PROMPT]' + system_message + '[/SYSTEM_PROMPT]' }}\n\n{%- for message in loop_messages %}\n {%- if message['role'] == 'user' %}\n\t {%- if message['content'] is string %}\n {{- '[INST]' + message['content'] + '[/INST]' }}\n\t {%- else %}\n\t\t {{- '[INST]' }}\n\t\t {%- for block in message['content'] %}\n\t\t\t {%- if block['type'] == 'text' %}\n\t\t\t\t {{- block['text'] }}\n\t\t\t {%- elif block['type'] == 'image' or block['type'] == 'image_url' %}\n\t\t\t\t {{- '[IMG]' }}\n\t\t\t\t{%- else %}\n\t\t\t\t {{- raise_exception('Only text and image blocks are supported in message content!') }}\n\t\t\t\t{%- endif %}\n\t\t\t{%- endfor %}\n\t\t {{- '[/INST]' }}\n\t\t{%- endif %}\n {%- elif message['role'] == 'system' %}\n {{- '[SYSTEM_PROMPT]' + message['content'] + '[/SYSTEM_PROMPT]' }}\n {%- elif message['role'] == 'assistant' %}\n {{- message['content'] + eos_token }}\n {%- else %}\n {{- raise_exception('Only user, system and assistant roles are supported!') }}\n {%- endif %}\n{%- endfor %}" self.model = Mistral3ForConditionalGeneration.from_pretrained( model_id, trust_remote_code=True, quantization_config=quantization_config, device_map="cuda", ) @app.infer def infer(self, request: RequestObjects) -> ResponseObjects: system_prompt = "You are a conversational agent that always answers straight to the point." if request.system_prompt is not None: system_prompt = request.system_prompt messages = [ { "role": "system", "content": system_prompt }, { "role": "user", "content": request.prompt }, ] tokenized_chat = self.tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda") with torch.no_grad(): generation = self.model.generate( tokenized_chat, max_new_tokens=request.max_tokens, temperature=request.temperature, top_p=request.top_p, top_k=request.top_k, do_sample=request.do_sample, repetition_penalty=request.repetition_penalty, ) generated_text = self.tokenizer.decode(generation[0], skip_special_tokens=True) return ResponseObjects(generated_text=generated_text) def finalize(self): self.model = None @inferless.local_entry_point def my_local_entry(dynamic_params): request_objects = RequestObjects(**dynamic_params) model_instance = InferlessPythonModel() return model_instance.infer(request_objects) ``` ### Step 2: Run with Remote GPU From your local terminal, navigate to the folder containing your `app.py` and your `inferless-runtime-config.yaml` and run: ```bash theme={null} inferless remote-run app.py -c inferless-runtime-config.yaml --prompt "Give me 5 non-formal ways to say 'See you later' in French." ``` You can pass the other input parameters in the same way (e.g., `--temperature`, `--system_prompt`, etc.) as long as your code expects them in the `inputs` dictionary. If you want to exclude certain files or directories from being uploaded, use the `--exclude` or `-e` flag. ## Method A: Deploying the model on Inferless Platform Inferless supports multiple ways of [importing your model](https://docs.inferless.com/model-import/file-structure-req/file-structure-requirements). For this tutorial, we will use GitHub. ### Step 1: Login to the inferless dashboard can click on Import model button Navigate to your desired workspace in Inferless and Click on `Add a custom model` button that you see on the top right. An import wizard will open up. ### Step 2: Follow the UI to complete the model Import * Select the GitHub/GitLab Integration option to connect your source code repository with the deployment environment. * Navigate to the specific GitHub repository that contains your model's code. Here, you will need to identify and enter the name of the model you wish to import. * Choose the appropriate type of machine that suits your model's requirements. Additionally, specify the minimum and maximum number of replicas to define the scalability range for deploying your model. * Optionally, you have the option to enable automatic build and deployment. This feature triggers a new deployment automatically whenever there is a new code push to your repository. * If your model requires additional software packages, configure the Custom Runtime settings by including necessary pip or apt packages. Also, set up environment variables such as Inference Timeout, Container Concurrency, and Scale Down Timeout to tailor the runtime environment according to your needs. * Wait for the validation process to complete, ensuring that all settings are correct and functional. Once validation is successful, click on the "Import" button to finalize the import of your model. ### Step 3: Wait for the model build to complete usually takes \~5-10 minutes ### Step 4: Use the APIs to call the model Once the model is in 'Active' status you can click on the 'API' page to call the model ### Here is the Demo: ## Method B: Deploying the model on Inferless CLI Inferless allows you to deploy your model using Inferless-CLI. Follow the steps to deploy using Inferless CLI. ### Clone the repository of the model Let's begin by cloning the model repository: ```bash theme={null} git clone https://github.com/inferless/mistral-small-3.1-24b-instruct.git ``` ### Deploy the Model To deploy the model using Inferless CLI, execute the following command: ```bash theme={null} inferless deploy --gpu A100 --runtime inferless-runtime-config.yaml ``` **Explanation of the Command:** * `--gpu A100`: Specifies the GPU type for deployment. Available options include `A10`, `A100`, and `T4`. * `--runtime inferless-runtime-config.yaml`: Defines the runtime configuration file. If not specified, the default Inferless runtime is used.