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---
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
<!-- Provide a longer summary of what this model is. -->
- **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]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### 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.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### 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
[More Information Needed]
- **Hardware Type:** 2 x Nvidia T4
- **Hours used:** 2h
- **Cloud Provider:** Kaggle
- **Compute Region:** Russia
- PEFT 0.14.0 |