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MyAwesomeModel-TestRepo

by Cdf xxxxxxxx11233/MyAwesomeModel-TestRepo

MyAwesomeModel-TestRepo is an open-weight model from Cdf, released under MIT License. Its published files total 9.1 KB.

The MyAwesomeModel has undergone a significant version upgrade. In the latest update, MyAwesomeModel has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic…

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Context—
Weights23 B
Licensemit
AccessOpen weights
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Model Card

By Cdf, published under mit, revision 6ed427da6013.

The MyAwesomeModel has undergone a significant version upgrade. In the latest update, MyAwesomeModel has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic. Its overall performance is now approaching that of other leading models. Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For instance, in the AIME 2025 test, the model’s accuracy has…

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MyAwesomeModel


1. Introduction

The MyAwesomeModel has undergone a significant version upgrade. In the latest update, MyAwesomeModel has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic. Its overall performance is now approaching that of other leading models.

Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For instance, in the AIME 2025 test, the model’s accuracy has increased from 70% in the previous version to 87.5% in the current version. This advancement stems from enhanced thinking depth during the reasoning process: in the AIME test set, the previous model used an average of 12K tokens per question, whereas the new version averages 23K tokens per question.

Beyond its improved reasoning capabilities, this version also offers a reduced hallucination rate and enhanced support for function calling.

2. Evaluation Results

Comprehensive Benchmark Results

| | Benchmark | Model1 | Model2 | Model1-v2 | MyAwesomeModel | |---|---|---|---|---|---| | **Core Reasoning Tasks** | Math Reasoning | 0.510 | 0.535 | 0.521 | 0.550 | | | Logical Reasoning | 0.789 | 0.801 | 0.810 | 0.819 | | | Common Sense | 0.716 | 0.702 | 0.725 | 0.736 | | **Language Understanding** | Reading Comprehension | 0.671 | 0.685 | 0.690 | 0.700 | | | Question Answering | 0.582 | 0.599 | 0.601 | 0.607 | | | Text Classification | 0.803 | 0.811 | 0.820 | 0.828 | | | Sentiment Analysis | 0.777 | 0.781 | 0.790 | 0.792 | | **Generation Tasks** | Code Generation | 0.615 | 0.631 | 0.640 | 0.650 | | | Creative Writing | 0.588 | 0.579 | 0.601 | 0.610 | | | Dialogue Generation | 0.621 | 0.635 | 0.639 | 0.644 | | | Summarization | 0.745 | 0.755 | 0.760 | 0.767 | | **Specialized Capabilities**| Translation | 0.782 | 0.799 | 0.801 | 0.804 | | | Knowledge Retrieval | 0.651 | 0.668 | 0.670 | 0.676 | | | Instruction Following | 0.733 | 0.749 | 0.751 | 0.758 | | | Safety Evaluation | 0.718 | 0.701 | 0.725 | 0.739 |

Overall Performance Summary

The MyAwesomeModel 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 MyAwesomeModel. Please check our official website for more details.

4. How to Run Locally

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

Compared to previous versions, the usage recommendations for MyAwesomeModel 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 MyAwesomeModel-Small is identical to its base model, but it shares the same tokenizer configuration as the main MyAwesomeModel. 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 MyAwesomeModel, a helpful AI assistant.
Today is {current date}.

For example,

You are MyAwesomeModel, a helpful AI assistant.
Today is May 28, 2025, Monday.

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 MyAwesomeModel 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 [email protected]. ```

Identity and Version

Repository
xxxxxxxx11233/MyAwesomeModel-TestRepo
Publisher
Cdf
Task
Not stated by the source
Modality
Other
Library
transformers
Parameters
Not stated by the source
Languages
Not stated by the source
Revision
6ed427da6013b9cf3d2cb73c67cd1ff0d9e39a48
First published
2026-09-26
Last updated
2026-09-26

Files and Weights

4 files, 9.1 KB in total. The weights are 1 file totalling 23 B in bin.

Weights1 file · 23 B
Configuration1 file · 66 B
Documentation1 file · 7.5 KB
Repository1 file · 1.5 KB
Every file
FileTypeSizeSHA-256
checkpoints/step_1000/pytorch_model.binWeights23 B 965362299a23
checkpoints/step_1000/config.jsonConfiguration66 B —
README.mdDocumentation7.5 KB —
.gitattributesRepository1.5 KB —

License and Download

License
mit
Access
Open weights, no gate
Download size
23 B
Download from Cdf

Released by Cdf through its official repository on Hugging Face. Read the license.

Memory Requirements

PrecisionWeights in memory
As published23 B

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About MyAwesomeModel-TestRepo

Can I use MyAwesomeModel-TestRepo commercially?

Yes. MyAwesomeModel-TestRepo is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.