---
language: [en]
license: mit
tags:
- supply-chain
- procurement
- logistics
- inventory
- vendor-management
- slm
- llama-style
- rope
- 1m-context
- from-scratch
- 1b-params
pipeline_tag: text-generation
---

# Supply Chain Manager-SLM: Role-Based Small Language Model

A **LLaMA-style transformer** (~989.2M params, ~0.99B) trained from scratch for the **Supply Chain Manager** role.
Supports up to **1M token context** via RoPE with gradient checkpointing.

## Architecture
| Component | Value |
|-----------|-------|
| Architecture | LLaMA-style (RoPE + RMSNorm + SwiGLU) |
| Parameters | ~989.2M (~0.99B) |
| Layers | 32 |
| Heads | 20 |
| Embedding | 1600 |
| Max Context | 100,000,000,000 tokens |
| Max Output | 1,000,000 tokens |
| Vocab | 1,715 BPE |
| Model Size | ~4 GB (fp32) |

## Training
- Best eval loss: 5.352407482803845
- Trained with gradient checkpointing on Apple M4 (MPS)
- 3 epochs, batch_size=1, grad_accum=16

## Usage
```python
from huggingface_hub import hf_hub_download
from tokenizers import Tokenizer

model_path = hf_hub_download("sathishphdai/supply-chain-manager-slm-1m", "model.safetensors")
tokenizer_path = hf_hub_download("sathishphdai/supply-chain-manager-slm-1m", "supply_chain_manager_tokenizer.json")
tokenizer = Tokenizer.from_file(tokenizer_path)
```