README.md

---
pipeline_tag: text-generation
library_name: transformers, datasets, PyTorch
language:
- ru
- en
- zh
license: mit 
base_model: AxiomAI_Axiom-Ask-1.0-3B
---
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

## Model Details

### Model Description

<!-- Мы AxiomAI-comunnity, группа подростков которые объединились. 
Мы из России и Китая. Вместе мы исследуем программирование и создаём 
нейросети с 26 февраля 2026 года. -->



- **Developed by:** [AxiomAI-comunnity]
- **Funded by [GenAI/Fantominsight]:** [AxiomAI]
- **Shared by [GenAI/Fantominsight]:** [AxiomAI]
- **Model type:** [LLM AI, transformers, 3B]
- **Language(s) (NLP):** [English, Russian, Chinese]
- **License:** [MIT]
- **Finetuned from model [none]:** [not Fine-tuned]

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [this repository]
- **Paper [not selected now]:** [try again later]
- **Demo [not selected now]:** [try again later]

## Uses

<!-- Telegram Bot API-->

### Direct Use

<!-- Telegram Bot-->

[https://t.me/axiom_ask_bot]

### Downstream Use [optional]

<!-- Not provided now. Try again later  -->

[Not provided now. Try again later
### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[Not provided now. Try again later]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[Not provided now. Try again later]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[Not provided now. Try again later]

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[Not provided now. Try again later]

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[Not provided now. Try again later]


#### Training Hyperparameters

- **Training regime:** [Not provided now. Try again later] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[Not provided now. Try again later]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[Not provided now. Try again later]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[Not provided now. Try again later]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[Not provided now. Try again later]

### Results

[Not provided now. Try again later]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[Not provided now. Try again later]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [GPU-server]
- **Hours used:** [0]
- **Cloud Provider:** [Hugging Face]
- **Compute Region:** [Pekin China, Moscow Russia]
- **Carbon Emitted:** [Not provided now. Try again later]

## Technical Specifications [optional]

### Model Architecture and Objective

[Not provided now. Try again later]

### Compute Infrastructure

[Not provided now. Try again later]

#### Hardware

[Not provided now. Try again later]

#### Software

[Not provided now. Try again later]

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[Not provided now. Try again later]

**APA:**

[Not provided now. Try again later]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[Not provided now. Try again later]

## More Information [optional]

[Not provided now. Try again later]

## Model Card Authors [optional]

[Not provided now. Try again later]

## Model Card Contact

[Telegram: https://t.me/developover]