README.md

---
library_name: transformers
tags: []
---

# Model Card for Model ID

This is a LoRA fine-tuned causal language model trained on a PyTorch Q&A dataset.
The base model was adapted using PEFT (Parameter-Efficient Fine-Tuning)
 with low-rank adapters.
It is designed to answer questions related to PyTorch concepts, APIs, and usage examples.



## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** Arush Sharma
- **Task:** Causal Language Modeling (Q&A style)
- **Finetuned from model:** Qwen/Qwen2.5-3B

### Model Sources [optional]

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

- **Repository:** https://github.com/Arush04/ML_Clutter/blob/main/PyTorch_Model.ipynb

## Uses


- Educational purposes for learning PyTorch
- Assisting developers with PyTorch-related queries
- Small-scale research and experimentation

## Training Details

#### Training Hyperparameters

- **Training regime:**

- LoRA rank (r): 32
- LoRA alpha: 32
- Dropout: 0.05
- Batch size: 2 (gradient accumulation: 8)
- Epochs: 2
- Optimizer: paged_adamw_8bit
- Precision: FP16
- Dataset: PyTorch Q&A dataset (custom curated) [https://huggingface.co/datasets/sharmaarush/Pytorch_QA]

## Evaluation

![image/png](https://cdn-uploads.huggingface.co/production/uploads/65b2ac56e4191ceeb406aa4b/A3xo8PCWdb9bJ7jC4cdet.png)

## How to Use

```
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from peft import PeftModel
tokenizer = AutoTokenizer.from_pretrained("sharmaarush/pytorch_QA_model")
base_model = AutoModelForCausalLM.from_pretrained(
    model,
    device_map="auto"
)
model = PeftModel.from_pretrained(base_model, "sharmaarush/pytorch_QA_model")
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device_map="auto")

# Run inference
prompt = "Explain FSDP2 in easier terms"
outputs = pipe(prompt, do_sample=True, temperature=0.7)
print(outputs[0]["generated_text"])
```