---
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
library_name: peft
license: apache-2.0
datasets:
- STEM-AI-mtl/Electrical-engineering
language:
- en
tags:
- Engineering
pipeline_tag: text-generation
---

# Model Card for Model ID

This lora is trained for Deepseek R1 Qwen model for providing better replies in Engineering using smaller LLMs.



## Model Details

### Model Description

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- **Developed by:** Just some student
- **Model type:** PEFT
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

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[More Information Needed]

### Out-of-Scope Use

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[More Information Needed]

## Bias, Risks, and Limitations

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[More Information Needed]

### Recommendations

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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.

[More Information Needed]

## Training Details

### Training Data

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[More Information Needed]

### Training Procedure

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->




#### Metrics

Train loss - 1.399200	Eval loss - 1.393927


[More Information Needed]

### Results

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- **Hardware Type:** 2 x Nvidia T4
- **Hours used:** 2h
- **Cloud Provider:** Kaggle
- **Compute Region:** Russia

- PEFT 0.14.0