✌️How to Create a Job?

Select Model

You select model from the Model Hub in two ways:

• Model Catalog: A repository of model sources from various providers such as DeepSeek, Gemma, Llama, and Qwen.

• Private Model: A repository for user-owned models and fine-tuned models. These models must already have all necessary files uploaded. Must include specific tags to indicate compatibility and readiness.

Note: To be used in Test Jobs, models must meet the following criteria:

• Model type:

  • LLM: Accepts only text input

  • VLM: Accepts both text and image input

• Model size > 0

• Learning stage: Must be Instruction-tuned

Example:

Model name	Model version	Model family	Model type	Model size	Learning stage
ft_Llama-3.1-8B_20250508124054_samples-15d5e2f6fe7	15d5e2f6fe7	Llama	LLM (→ available)	8B (→ available)	Instruction-tuned (→ available)

Select Test Suite

Select the appropriate test suite - which tests the model.

We offer this test suite:

Test suite	Purpose	Best for
Standard	Evaluate models using your own dataset.	Internal benchmarks, domain-specific tasks (e.g., finance, medical...)
Nejumi Leaderboard 3	Benchmark LLMs, especially for Japanese language tasks. Reference: Nejumi Leaderboard 3	Comparing LLMs on Japanese language tasks.
LM Evaluation Harness	General framework to benchmark language models across many standard NLP benchmarks. Reference: LM Evaluation Harness	Evaluating English-centric LLMs and ensuring comparability with research literature
VLM Evaluation Kit	Evaluate VLMs (Vision Language Models) on multimodel tasks. Reference: VLMEvalKit	Testing multimodal models

Standard

Select Data Format

Only select data format when you choose test suite = standard

Supported data format	Supported file format	Supported file size
Alpaca	- CSV - JSON - JSONLINES - ZIP - PARQUET	Limit 100MB
ShareGPT	- JSON - JSONLINES - ZIP - PARQUET	Limit 100MB
ShareGPT_Image	- ZIP - PARQUET	Limit 100MB

We currently support data formats for testing include:

a/ Alpaca

Alpaca uses a very simple structure to fine-tune the model with Instruction-following format with input, output pairs for supervised fine-tuning tasks. The basic structure includes:

• Instruction: A string containing the specific task or request that the model needs to perform.

• Input: A string containing the information that the model needs to process in order to carry out the task.

• Output: A string representing the result the model should return, generated from processing the instruction and input.

Detailed Structure:

Copy[
   {
      “instruction”: “string”,
      “input”: “string”,
      “output” “string”
  }
]

Examples:

Copy[
    {
        "instruction": "In this task, you are given Wikipedia articles on a range of topics as passages and a question from the passage. We ask you to answer the question by classifying the answer as 0 (False) or 1 (True)\n\nNow complete the following instance -\nInput: Passage: Missouri River -- Tonnage of goods shipped by barges on the Missouri River has seen a serious decline from the 1960s to the present. In the 1960s, the USACE predicted an increase to 12 million short tons (11 Mt) per year by 2000, but instead the opposite has happened. The amount of goods plunged from 3.3 million short tons (3.0 Mt) in 1977 to just 1.3 million short tons (1.2 Mt) in 2000. One of the largest drops has been in agricultural products, especially wheat. Part of the reason is that irrigated land along the Missouri has only been developed to a fraction of its potential. In 2006, barges on the Missouri hauled only 200,000 short tons (180,000 t) of products which is equal to the amount of daily freight traffic on the Mississippi.\nQuestion: is there barge traffic on the missouri river\nOutput:",
        "input": "",
        "output": "1"
    },
    {
        "instruction": "CARAÏBES Communauté des Caraïbes (CARICOM) Aperçu Les 15 membres de la Communauté des Caraïbes (CARICOM) sont Antigua et Barbuda, les Bahamas, la Barbade, le Belize, la République dominicaine, la Grenade, la Guyane, Haïti, la Jamaïque, Saint-Kitts-et-Nevis, Sainte-Lucie, Saint Vincent et les Grenadines, le Suriname, Trinité-et-Tobago, ainsi que Montserrat (sous la dépendance du Royaume-Uni).\n\nWhich language is this?",
        "input": "",
        "output": "French"
    }
]

Samples: https://github.com/fpt-corp/ai-studio-samples/tree/main/sample-datasets/alpaca

Supported file format: .csv, .json, .jsonlines, .zip, .parquet

b/ ShareGPT

ShareGPT is designed to represent multi-turn conversations (back-and-forth chats) between a user and an AI assistant. It is commonly used when training or fine-tuning models for dialogue systems or chatbots that need to handle contextual conversation over multiple turns.

Each data sample consists of a conversations array, where each turn in the chat includes:

• from: Who is speaking — usually "human" or "system".

• value: The actual message text from that speaker.

Detailed Structure:

Copy[
   {
      “conversations”: [
         {
            “from”: “string”,
            “value”: “string”
         }
      ]
   }
]

Examples:

Copy[
    {
        "conversations": [
            {
                "from": "system",
                "value": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."
            },
            {
                "from": "human",
                "value": "Select the latest conversation state of the provided conversation history. Response only with the conversation state index.\n\n# Context\n- Hôm nay: Thứ tư, 19/02\n- Họ tên khách hàng: LÊ HOÀNG LOAN\n- Sản phẩm vay: Điện thoại Samsung\n- Số tiền cần thanh toán kỳ này: 1515000\n- Ngày đến hạn thanh toán kỳ này: 16/02\n- Số ngày đã trễ hạn thanh toán: 3\n- Hạn thanh toán cuối: 21/02\n\n# Conversation history\nTổng đài viên: Dạ có phải Anh LOAN đang nghe máy không ạ?\nNgười nhận cuộc gọi: có gì không em\n\n# Conversation states\n1) Xác nhận danh tính của mình là khách hàng - Ví dụ: \"ừ ai đấy\", \"phải chị\", \"đúng rồi ai gọi thế\", \"ờ vâng\", \"dạ vâng\", \"Anh LOAN đây\", \"sao thế\", \"mình nghe ạ\", \"có chuyện gì\", \"nói đi\"\n2) Hỏi danh tính của tổng đài viên - Ví dụ: \"ai đấy\", \"bạn tên gì\", \"bạn là ai\", \"công ty gì\", \"lừa đảo à\"\n3) Xác nhận là người thân (như ông, bà, bố, mẹ, cô, dì, chú, bác, anh, chị, em, vợ, con) của khách hàng hoặc có quen biết khách hàng - Ví dụ: \"anh là anh nó\", \"mình là chị của LOAN\", \"bác là mẹ nó nghe máy hộ nó đây\", \"cô là dì nó\", \"em là người nhà của Anh LOAN\", \"bạn ấy ra ngoài rồi mình là bạn của bạn ấy\", \"à Anh làm cùng cơ quan với nó\", \"mình là bạn nó\", \"quen biết qua thôi\"\n4) Thông báo không quen biết khách hàng hoặc thông báo nhầm số / nhầm người hoặc thông báo khách hàng đi vắng / chỉ nghe máy hộ và không cung cấp thông tin về mối quan hệ với khách hàng - Ví dụ: \"có quen biết gì đâu\", \"mình không biết bạn đấy\", \"ủa số này của tôi mà có biết đấy là ai đâu\", \"tôi nghe hộ bạn ấy ra ngoài rồi\", \"không nhầm rồi em ơi\", \"LOAN nào nhỉ\", \"LOAN á không phải rồi\", \"ai cơ\"\n5) Đồng ý thanh toán chung chung hoặc thông báo thời điểm hoặc số tiền sẽ thanh toán - Ví dụ: \"à ờ\", \"đúng rồi\", \"biết rồi nhé\", \"lát nữa nhé\", \"tý nữa giả nhé\", \"mai Anh đóng\", \"để tôi sắp xếp\", \"để hỏi nhờ người nhà xem sao\", \"giờ chưa có nhưng mai sẽ trả\", \"ngày kia trả được không nhỉ\"\n6) Đặt câu hỏi hoặc thắc mắc về khoản nợ, số hotline, phương thức thanh toán, ngày tháng - Ví dụ: \"tôi vay sản phẩm nào ấy nhỉ\", \"Anh cần trả bao nhiêu tiền ấy nhỉ\", \"Anh chưa biết cấch thanh toán như thế nào ấy\", \"hợp đồng của tôi còn bao nhiêu kỳ nữa\", \"ơ hạn thanh toán là ngày mùng sáu hàng tháng mà nhỉ\", \"sao lại nhiều tiền thế nhỉ\", \"sao tháng này cần đóng nhiều tiền hơn tháng trước\", \"lãi suất quá hạn là bao nhiêu hả em\", \"Anh đóng luôn mấy kỳ còn lại được không\", \"hôm nay ngày mấy nhỉ\", \"số điện thoại phản ánh là số mấy\", \"không biết đóng ở cửa hàng nào\"\n7) Báo bận hoặc đang không tiện nghe máy hoặc hẹn gọi lại sau - Ví dụ: \"Anh đang bận nhé\", \"tôi đang đi làm bạn ơi\", \"giờ không tiện lắm gọi lại sau đi\", \"tôi đang ngoài đường gọi lại sau nhé\"\n8) Phàn nàn hoặc khiếu nại do bị gọi nhiều - Ví dụ: \"bên em gọi nhiều thế\", \"sao một ngày gọi chục cuộc thế hả em\", \"đừng có gọi nữa mệt lắm rồi\"\n9) Không nghe rõ hoặc yêu cầu nhắc lại thông tin - Ví dụ: \"alo em nói gì thế\", \"Anh chưa nghe được em ơi\", \"ừ cái gì ấy nhỉ\", \"bạn nói lại giúp mình với\", \"ồn quá mình không nghe được ấy\", \"không nói gì à\"\n10) Thông báo khách hàng đã qua đời - Ví dụ: \"bạn ấy bị tại nạn mới mất rồi\", \"nó qua đời rồi em ạ\", \"Anh LOAN chết rồi bạn ơi\"\n11) Muốn gặp tổng đài viên - Ví dụ: \"cho tôi nói chuyện với tổng đài bên bạn nào\", \"Anh muốn gặp tổng đài viên\", \"chuyển máy cho tổng đài xử lý được không\"\n12) Trả lời sai trọng tâm của câu hỏi trước đó hoặc cung cấp thông tin chưa rõ ràng hoặc cần xác nhận lại thông tin hoặc đặt yêu cầu ngoài phạm vi hỗ trợ\n\n# Answer:"
            }
        ]
    }
]
[
    {
        "conversations": [
            {
                "from": "human",
                "value": "Summarize the main ideas of Jeff Walker's Product Launch Formula into bullet points as it pertains to a growth marketing agency implementing these strategies and tactics for their clients..."
            },
            {
                "from": "gpt",
                "value": "Here are the main ideas of Jeff Walker's Product Launch Formula that can be applied by a growth marketing agency for their clients:\n\n1. Identify the target audience and their needs: Understand the ideal customer for the product or service, and create a messaging that resonates with them.\n2. Pre-launch: Build anticipation and excitement for the launch by creating buzz, gathering testimonials and case studies, and using social media to create awareness.\n3. Launch: Use a well-crafted launch sequence to maximize sales and conversions. This can include offering bonuses, creating scarcity, and using a deadline to create urgency.\n4. Post-launch: Follow up with customers, gather feedback, and continue to provide value to keep them engaged and loyal.\n5. Create a product suite: Once the initial product is successful, expand the offering by creating additional products or services that address other needs of the same audience.\n6. Continual optimization: Continually monitor and optimize the launch process and product suite to improve results.\n7. Build a community: Use the launch process to build a community of customers who are passionate about the product and can help spread the word.\n8. Use automation: Use technology and automation to streamline the launch process and improve efficiency."
            }
        ]
    }
]

Samples: https://github.com/fpt-corp/ai-studio-samples/tree/main/sample-datasets/sharegpt

Supported file format: .json, .jsonlines, .zip, .parquet

c/ ShareGPT_Image

ShareGPT_Image is an extension of the ShareGPT multi-turn chat format, designed specifically for multi-modal training — that is, training models that handle both text and images in conversations.

It’s used in fine-tuning vision-language models (VLMs), which need to process images alongside natural language.

The structure includes:

• A list of chat turns under "message" (same as ShareGPT).

• A field called "image" or "image_path" that points to the image used in the conversation (using format png, jpg, jpeg)

Notice:

• Must include the image token in the chat content where an image should appear.

• If there are multiple images:

  • Image paths must be defined in an images array.

  • The positions of the images in the chat flow are indicated by the image tokens

  • The number of image tokens in the chat must match the number of items in the images array.

  • Images will be mapped in order of appearance, with each image token replaced by the corresponding image from the images array.

Detailed Structures:

Copy[
   {
      “message”: [
         {
            “role”: “string”, “content”: “<image>string”
         }
      ],
      “images”: [
         “images/0.jpg”
      ]
   }
]

Examples:

Copy[
    {
        "messages": [
            {
                "role": "user",
                "content": "<image>How many baseball players are visible in the image?"
            },
            {
                "role": "assistant",
                "content": "There are three baseball players visible in the image."
            }
        ],
        "images": [
            "images/0.jpg"
        ]
    },
    {
        "messages": [
            {
                "role": "user",
                "content": "What is the man wearing in the image?<image>"
            },
            {
                "role": "assistant",
                "content": "The man in the image is wearing chaps, a cowboy hat, and is dressed like a cowboy."
            }
        ],
        "images": [
            "images/1.jpg"
        ]
    },
    {
        "messages": [
            {
                "role": "user",
                "content": "<image>Explain the visual content of the image in great detail."
            },
            {
                "role": "assistant",
                "content": "The image presents a view of a hotel room, showing both the bathroom and bedroom areas. In the bathroom, there is a white sink under a mirror, accompanied by several bottles placed on the counter, and two cups nearby. \n\nLooking through a doorway into the bedroom area, a large bed can be seen dominating the space. A chair is also visible within the room, situated near the foot of the bed. The hair drier is located in the foreground, implying that it likely belongs to the bathroom area as well. The arrangement of the objects and the furniture suggests a comfortable and well-equipped hotel room."
            }
        ],
        "images": [
            "images/2.jpg"
        ]
    }
]

Samples: https://github.com/fpt-corp/ai-studio-samples/tree/main/sample-datasets/sharegpt-image

Supported file format: .zip, .parquet

Select Test Data

You have two ways to transfer the Test data:

• Connect to Data Hub

  1. Click Data Hub

  2. Select a connection or dataset from the Data Hub. Notice: Ensure the dataset is compatible with the selected format.

  3. (Optional) Click Open Data Hub to preview or manage datasets.

  4. (Optional) Click Reload icon to update connection and dataset list.

  5. Follow the detailed guide Data Hub

Supported data format	Supported file format	Supported file size
Alpaca	- CSV - JSON - JSONLINES - ZIP - PARQUET	Limit 100MB
ShareGPT	- JSON - JSONLINES - ZIP - PARQUET	Limit 100MB
ShareGPT_Image	- ZIP - PARQUET	Limit 100MB

Notice: Ensure the file matches the selected data format

• Upload a file

  1. Default value Upload file

  2. Choose a local file from your computer.

  3. (Optional) Click Download sample to see an example of the expected format.

Test Criteria

1. Click Add & update button

2. The Tasks window appears. Select the task type:

   • Text similarity: Measures similarity metrics between model outputs and reference texts.

3. Click Next to open the list of available metrics

4. Select one or more metrics

5. Click Update to apply changes

The following metrics of Text similarity are available:

Test criteria / Metric	How it tests	Best for
BLEU	Measures how precisely a model’s output matches reference text using n-gram overlap.	Evaluating translation and short text similarity.
Fuzzy Match	Measures how closely the model’s output resembles the reference text, allowing for minor differences in wording or order.	Checking approximate correctness.
ROUGE-1	Measures unigram (single word) overlap between model output and reference text.	Short summarization and text generation tasks.
ROUGE-2	Measures bigram (two-word sequence) overlap between model output and reference text.	Evaluating contextual accuracy.
ROUGE-L	Measures the longest common subsequence (LCS) between model output and reference text to capture fluency and word order similarity.	Longer text evaluation where structure matters.
ROUGE-LSUM	Measures LCS-based similarity across multiple sentences, suitable for evaluating longer summaries.	Summarization tasks.

Others

Set up Parameters

Parameter allows you to adjust the behavior of your model during testing. Below is a breakdown of each parameter and its purpose:

Name	Description	Type	Supported value
Log samples	Model’s outputs, and the text fed into the model, will be saved	bool	True / False
Max tokens	Maximum number of tokens to generate	int	(0, +∞)
No. of few-shot	Sets the number of few-shot examples to place in context. Must be an integer.	int	[0, +∞)
Temperature	The temperature for sampling	float	[0, +∞)
Repetition penalty	Float that penalizes new tokens based on whether they appear in the prompt and the generated text so far. - Values > 1 encourage new tokens. - Values < 1 encourage repetition.	float	(0, 5)
Seed	Random seed for reproducibility	int	[0, +∞)
Top-K	Integer that controls the cumulative probability of the top tokens to consider. Set to -1 to consider all tokens.	int	-1 or (0, +∞)
Top-P	Float that controls the cumulative probability of the top tokens to consider. Set to 1 to consider all tokens.	float	(0, 1]

Finish

Finally, you must enter a unique name job (e.g., testj_20250919145022) that satisfies the following conditions:

• Start with a letter or number

• Only use letters (a-z, A-Z), numbers (0-9), underscores “_” and hyphens “-”

• Maximum 100 characters

• Unduplicated name

Optional note job description with maximum 200 characters and receive an email notification ([email protected]) when pipeline is SUCCEEDED or FAILED.