---

language:

- hi

license: mit

library_name: gguf

tags:

- hindi

- story-generation

- gguf

- llama-cpp

- llama-style

- text-generation

base_model: SmallScale/Simple-Stories-Hindi-10M

datasets:

- SmallScale/Simple-Stories-Hindi

pipeline_tag: text-generation

---

📖 Simple-Stories-Hindi-10M-GGUF (FP16)

This repository contains the FP16 GGUF model file for SmallScale/Simple-Stories-Hindi-10M, a 11.45M parameter decoder-only Transformer model trained from scratch on the SmallScale/Simple-Stories-Hindi dataset (~2.11M Hindi stories).

---

📌 Model Information & Base Model

• Base Model: SmallScale/Simple-Stories-Hindi-10M
• Dataset Used: SmallScale/Simple-Stories-Hindi (~2.11M simple Hindi stories)
• Best Validation Loss: 1.8157 (at step 202,000)

---

📁 Model Files

| File Name | Quantization | Size | Description |

|---|---|---|---|

| Simple-Stories-Hindi-11M-F16.gguf | F16 (16-bit Float) | ~22.0 MB | Full 16-bit float precision GGUF format |

---

🏗️ Model Architecture Details

| Parameter | Value |

|---|---|

| Parameters | 11.45M total (10.17M non-embedding) |

| Architecture | LLaMA-style (RoPE + SwiGLU + RMSNorm) |

| Context Length | 512 tokens |

| Embedding Dim (d_model) | 320 |

| FFN Hidden Dim | 896 |

| Layers | 7 |

| Attention Heads | 5 |

| Vocabulary | 4,000 (SentencePiece Unigram) |

---

🚀 Quick Start / Usage

1. Using with llama.cpp CLI

./llama-cli -m Simple-Stories-Hindi-11M-F16.gguf \
    -p "एक समय की बात है" \
    -n 150 \
    --temp 0.8 \
    --top-k 40 \
    --top-p 0.95

2. Using with llama-cpp-python

from llama_cpp import Llama

llm = Llama(
    model_path="Simple-Stories-Hindi-11M-F16.gguf",
    n_ctx=512,
    verbose=False
)

output = llm(
    "एक समय की बात है",
    max_tokens=150,
    temperature=0.8,
    top_k=40,
    top_p=0.95
)

print(output["choices"][0]["text"])

---

📄 License

MIT License