---
license: mit
---


from pathlib import Path
from huggingface_hub import HfApi

repo_id = "rekhi/SLM"

readme = """---
library_name: pytorch
license: mit
tags:
- pytorch
- custom-model
- slm
pipeline_tag: text-generation
---

# SLM

Lightweight SLM weights (`slm_model.pt`).  
> Replace this section with: architecture, training data, intended use, and limitations.

## Usage

```python
import torch
from huggingface_hub import hf_hub_download

# Download the file
ckpt_path = hf_hub_download(repo_id="rekhi/SLM", filename="slm_model.pt")

# 1) If this is a full torch model (saved with torch.save(model)):
model = torch.load(ckpt_path, map_location="cpu")
model.eval()

# 2) If this is a state_dict (saved with torch.save(model.state_dict())):
# Define your model class exactly as used in training, then:
# model = MyModel(**config)
# model.load_state_dict(torch.load(ckpt_path, map_location="cpu"))
# model.eval()