dusersad12/MyBrilliantModel-EvalRepo

MyBrilliantModel

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1. Introduction

The MyBrilliantModel has just shipped a major version upgrade. In this release, MyBrilliantModel deepens its reasoning and inference capabilities by drawing on more compute during post-training and a redesigned optimization schedule. It posts strong results across a wide spread of benchmark evaluations — mathematics, programming, open-domain knowledge, and general logic — and now sits comfortably among the leading open models.

Compared to the previous version, the upgraded model handles complex reasoning noticeably better. On the GSM8K 2026 test set, for example, accuracy climbed from 68.2% in the previous version to 84.9% in this one — driven by deeper thinking during the reasoning pass, where the average tokens per question rose from 14K in the old model to 26K in the new one.

Beyond reasoning, this version cuts the hallucination rate and adds stronger support for function calling and tool use.

2. Evaluation Results

Comprehensive Benchmark Results

	Benchmark	BaseLM	BaseLM-Plus	RivalLM	MyBrilliantModel
Core Reasoning Tasks	Math Reasoning	0.488	0.521	0.562	0.594
	Logical Reasoning	0.571	0.602	0.641	0.664
	Common Sense	0.612	0.651	0.698	0.720
	Abstract Reasoning	0.518	0.557	0.601	0.633
Language Understanding	Reading Comprehension	0.589	0.624	0.665	0.687
	Question Answering	0.547	0.581	0.622	0.655
	Text Classification	0.614	0.648	0.683	0.701
	Sentiment Analysis	0.662	0.697	0.731	0.750
	Paraphrase Detection	0.584	0.619	0.658	0.687
Generation Tasks	Code Generation	0.481	0.514	0.556	0.594
	Creative Writing	0.535	0.572	0.611	0.640
	Dialogue Generation	0.541	0.578	0.618	0.643
	Summarization	0.591	0.626	0.664	0.687
Specialized Capabilities	Translation	0.704	0.738	0.766	0.788
	Knowledge Retrieval	0.587	0.622	0.661	0.687
	Instruction Following	0.638	0.673	0.711	0.730
	Safety Evaluation	0.574	0.608	0.641	0.658

Overall Performance Summary

The MyBrilliantModel demonstrates strong performance across all evaluated benchmark categories, with particularly notable results in reasoning and generation tasks.

3. Chat Website & API Platform

We offer a chat interface and API for you to interact with MyBrilliantModel. Please check our official website for more details.

4. How to Run Locally

Please refer to our code repository for more information about running MyBrilliantModel locally.

Compared to previous versions, the usage recommendations for MyBrilliantModel have the following changes:

1. System prompt is supported.
2. It is not required to add special tokens at the beginning of the output to force the model into a specific thinking pattern.

The model architecture of MyBrilliantModel-Small is identical to its base model, but it shares the same tokenizer configuration as the main MyBrilliantModel. This model can be run in the same manner as its base model.

System Prompt

We recommend using the following system prompt with a specific date.

You are MyBrilliantModel, a helpful AI assistant.
Today is {current date}.

For example,

You are MyBrilliantModel, a helpful AI assistant.
Today is Jul 09, 2026, Thursday.

Temperature

We recommend setting the temperature parameter $T_{model}$ to 0.6.

Prompts for File Uploading and Web Search

For file uploading, please follow the template to create prompts, where {file_name}, {file_content} and {question} are arguments.

file_template = \
"""[file name]: {file_name}
[file content begin]
{file_content}
[file content end]
{question}"""

For web search enhanced generation, we recommend the following prompt template where {search_results}, {cur_date}, and {question} are arguments.

search_answer_en_template = \
'''# The following contents are the search results related to the user's message:
{search_results}
In the search results I provide to you, each result is formatted as [webpage X begin]...[webpage X end], where X represents the numerical index of each article. Please cite the context at the end of the relevant sentence when appropriate. Use the citation format [citation:X] in the corresponding part of your answer. If a sentence is derived from multiple contexts, list all relevant citation numbers, such as [citation:3][citation:5]. Be sure not to cluster all citations at the end; instead, include them in the corresponding parts of the answer.
When responding, please keep the following points in mind:
- Today is {cur_date}.
- Not all content in the search results is closely related to the user's question. You need to evaluate and filter the search results based on the question.
- For listing-type questions (e.g., listing all flight information), try to limit the answer to 10 key points and inform the user that they can refer to the search sources for complete information. Prioritize providing the most complete and relevant items in the list. Avoid mentioning content not provided in the search results unless necessary.
- For creative tasks (e.g., writing an essay), ensure that references are cited within the body of the text, such as [citation:3][citation:5], rather than only at the end of the text. You need to interpret and summarize the user's requirements, choose an appropriate format, fully utilize the search results, extract key information, and generate an answer that is insightful, creative, and professional. Extend the length of your response as much as possible, addressing each point in detail and from multiple perspectives, ensuring the content is rich and thorough.
- If the response is lengthy, structure it well and summarize it in paragraphs. If a point-by-point format is needed, try to limit it to 5 points and merge related content.
- For objective Q&A, if the answer is very brief, you may add one or two related sentences to enrich the content.
- Choose an appropriate and visually appealing format for your response based on the user's requirements and the content of the answer, ensuring strong readability.
- Your answer should synthesize information from multiple relevant webpages and avoid repeatedly citing the same webpage.
- Unless the user requests otherwise, your response should be in the same language as the user's question.
# The user's message is:
{question}'''

5. License

This code repository is licensed under the MIT License. The use of MyBrilliantModel models is also subject to the MIT License. The model series supports commercial use and distillation.

6. Contact

If you have any questions, please raise an issue on our GitHub repository or contact us at contact@mybrilliantmodel.ai. ```