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SmallScale/Simple-Stories-Hindi-20M-GGUF
Simple-Stories-Hindi-20M-GGUF is a text generation model from SmallScale. Use it when you need the model to write or continue text. It is set up for gguf. The card lists the license as mit.
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.
Downloads · 30 days
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.gguf44.8 MB · 100%
From the Hugging Face model README
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.
| 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 |
| 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) |
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
llama-cpp-pythonfrom 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"])
Try the live browser demo powered by WebAssembly & WebGPU: SmallScale/simple-stories-20m-webgpu
MIT