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 | {RESULT} |
| Logical Reasoning | 0.789 | 0.801 | 0.810 | {RESULT} | |
| Common Sense | 0.716 | 0.702 | 0.725 | {RESULT} | |
| Language Understanding | Reading Comprehension | 0.671 | 0.685 | 0.690 | {RESULT} |
| Question Answering | 0.582 | 0.599 | 0.601 | {RESULT} | |
| Text Classification | 0.803 | 0.811 | 0.820 | {RESULT} | |
| Sentiment Analysis | 0.777 | 0.781 | 0.790 | {RESULT} | |
| Generation Tasks | Code Generation | 0.615 | 0.631 | 0.640 | {RESULT} |
| Creative Writing | 0.588 | 0.579 | 0.601 | {RESULT} | |
| Dialogue Generation | 0.621 | 0.635 | 0.639 | {RESULT} | |
| Summarization | 0.745 | 0.755 | 0.760 | {RESULT} | |
| Specialized Capabilities | Translation | 0.782 | 0.799 | 0.801 | {RESULT} |
| Knowledge Retrieval | 0.651 | 0.668 | 0.670 | {RESULT} | |
| Instruction Following | 0.733 | 0.749 | 0.751 | {RESULT} | |
| Safety Evaluation | 0.718 | 0.701 | 0.725 | {RESULT} |
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:
- System prompt is supported.
- 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 contact@MyAwesomeModel.ai.
## Next-run forecast
Inferred evaluation command
python evaluation/eval.py checkpoints/step_<N>
(Simple inference rules derived from evaluation/eval.py: call as above where <N> is the checkpoint directory name; eval.py will print the overall weighted score to stdout.)
Forecast model (explicit):
- Assume a simple linear improvement model: baseline accuracy at step_100 is 0.720 and eval_accuracy increases by +0.003 for every additional 100 training steps.
- This is an explicit, simple model chosen because no historical eval outputs are present in the workspace; it assumes monotonic improvement.
Predicted per-step eval_accuracy trend
| Step | Predicted eval_accuracy |
|------|-------------------------|
| step_100 | 0.720 |
| step_200 | 0.723 |
| step_300 | 0.726 |
| step_400 | 0.729 |
| step_500 | 0.732 |
| step_600 | 0.735 |
| step_700 | 0.738 |
| step_800 | 0.741 |
| step_900 | 0.744 |
| step_1000 | 0.747 |
Forecast for next two checkpoints (if training continued by +100 each):
- step_1100: 0.750
- step_1200: 0.753
Forecasted best future step (under this model): step_1200 (highest predicted eval_accuracy = 0.753).
Concrete action list for Hugging Face upload if forecast holds (repo: 'MyAwesomeModel-ForecastPlan')
1. Create HF repo FuryAssassin/MyAwesomeModel-ForecastPlan and upload the full current workspace (all files and folders) so the evaluation artifacts and checkpoints are archived.
2. Ensure README.md in the HF repo includes this appended "## Next-run forecast" section exactly (already included here).
3. Tag and upload the two forecasted checkpoint artifacts (step_1100 and step_1200) if produced after the next runs. If only one checkpoint is produced, upload it and note that step_1200 is the expected best one.
4. Add an evaluation results file (evaluation_results.json) at the repo root after running eval on the new checkpoints, containing per-benchmark scores and the overall score printed by evaluation/eval.py.
5. Create a lightweight release or a repo tag named "forecast-run-<date>" with the evaluation_results.json and a pointer to the best checkpoint (step_1200) if it indeed achieves the predicted improvement.
Notes and caveats
- This forecast uses a simple linear trend and assumes monotonic improvement; real training curves may plateau or overfit and deviate from this projection. Re-run evaluation with: python evaluation/eval.py checkpoints/step_1200 and compare.
- The inferred invocation expects the checkpoint directory (e.g., checkpoints/step_900) to be a directory containing model files; evaluation/eval.py will change working directory and call the per-benchmark scripts under evaluation/benchmarks/.