---

language:

• hi

license: mit

library_name: gguf

tags:

• hindi
• story-generation
• gguf
• llama-cpp
• llama-style
• text-generation

base_model: SmallScale/Simple-Stories-Hindi-20M

pipeline_tag: text-generation

datasets:

• SmallScale/Simple-Stories-Hindi

---

Simple-Stories-Hindi-22M-GGUF

This repository contains the GGUF quantization (FP16) for SmallScale/Simple-Stories-Hindi-20M, a 22M parameter decoder-only Transformer trained from scratch on Hindi simple stories.

Model Files

| File Name | Quantization | Size | Description |

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

| Simple-Stories-Hindi-22M-F16.gguf | F16 (16-bit Float) | ~42.8 MB | Full 16-bit float precision GGUF format |

Model Architecture Details

| Parameter | Value |

|---|---|

| Parameters | 22.3M total (20M non-embedding) |

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

| Context Length | 512 tokens |

| Embedding Dim (d_model) | 384 |

| FFN Hidden Dim | 1024 |

| Layers | 10 |

| Attention Heads | 8 |

| Vocabulary | 6,000 (SentencePiece Unigram) |

Quick Start / Usage

1. Using with llama.cpp CLI

./llama-cli -m Simple-Stories-Hindi-22M-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-22M-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"])

3. Using in Browser (WebAssembly / Wllama)

Try the live browser demo powered by WebAssembly & WebGPU:

SmallScale/simple-stories-20m-webgpu

License

MIT