Instructions to use SmallScale/Simple-Stories-Hindi-20M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SmallScale/Simple-Stories-Hindi-20M with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SmallScale/Simple-Stories-Hindi-20M", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("SmallScale/Simple-Stories-Hindi-20M", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SmallScale/Simple-Stories-Hindi-20M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SmallScale/Simple-Stories-Hindi-20M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SmallScale/Simple-Stories-Hindi-20M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SmallScale/Simple-Stories-Hindi-20M

SGLang

How to use SmallScale/Simple-Stories-Hindi-20M with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "SmallScale/Simple-Stories-Hindi-20M" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SmallScale/Simple-Stories-Hindi-20M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "SmallScale/Simple-Stories-Hindi-20M" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SmallScale/Simple-Stories-Hindi-20M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SmallScale/Simple-Stories-Hindi-20M with Docker Model Runner:
```
docker model run hf.co/SmallScale/Simple-Stories-Hindi-20M
```

Simple-Stories-Hindi-20M / README.md

kaushik-harsh-99

Update README with prominent evaluation metrics, architecture details and sample outputs

2fd3015 verified 1 day ago

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metadata

language:
  - hi
license: mit
library_name: transformers
tags:
  - hindi
  - story-generation
  - causal-lm
  - llama-style
  - transformer
  - from-scratch
  - text-generation
datasets:
  - SmallScale/Simple-Stories-Hindi
pipeline_tag: text-generation
model-index:
  - name: Simple-Stories-Hindi-20M
    results: []

📖 Simple-Stories-Hindi-20M (22.3M Parameters)

A 22.3M parameter decoder-only Transformer language model trained from scratch on 2.11 million Hindi simple stories. The model generates coherent, creative, and grammatically sound Hindi stories given a short text prompt.

📊 Evaluation & Training Metrics

Metric / Property	Value
Best Validation Loss	`1.6914` (Cross-Entropy Loss)
Total Training Steps	386,000 steps
Total Parameters	22,310,784 (22.3M)
Non-Embedding Parameters	20,006,784 (20M)
Training Dataset	SmallScale/Simple-Stories-Hindi (~2.11M stories)
Model Size on Disk	~86 MB (`model.safetensors`)

🏗️ Model Architecture Details

Parameter	Value	Notes
Architecture	LLaMA-style Decoder	RoPE + SwiGLU + RMSNorm
Hidden Size (`d_model`)	384	Vector dimension
FFN Intermediate Size	1024	8/3 × `d_model` rounded to multiple of 64
Layers (`n_layers`)	10	Transformer blocks
Attention Heads (`n_heads`)	8	Multi-Head Self Attention
Context Length (`max_seq_len`)	512 tokens	Sequence window
Vocabulary Size	6,000	SentencePiece Unigram (Devanagari optimized)
Weight Tying	Enabled	Token embeddings & output projection share weights
Precision	float32	Weights stored in native FP32 safetensors

🚀 Quick Start & Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load tokenizer and model directly from Hugging Face
tokenizer = AutoTokenizer.from_pretrained("SmallScale/Simple-Stories-Hindi-20M", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("SmallScale/Simple-Stories-Hindi-20M", trust_remote_code=True)

if torch.cuda.is_available():
    model = model.to("cuda")

# Prompt input
prompt = "एक समय की बात है"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

# Generate story
outputs = model.generate(
    **inputs,
    max_new_tokens=200,
    do_sample=True,
    top_k=40,
    top_p=0.95,
    temperature=0.8
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

📝 Sample Generated Stories

Prompt: एक समय की बात है

एक समय की बात है, और वह एक रात एक लड़की के लिए सब कुछ बदल सकती है। वह एक आभासी क्षेत्र में प्रवेश कर गई, जहाँ वह एक लड़के से मिली, जो उसके सपनों से बना था। वे अपने डर और इच्छाओं को साझा करते थे...

Prompt: एक जंगल में

एक जंगल में जहाँ पेड़ों ने रहस्यों को फुसफुसाया, एक लड़का एक छोटी सी झोपड़ी में रहता था। वह अक्सर सोचता था कि अगर वह अपने सपनों में एक परी से मिल सकता है तो क्या होगा...

🔗 Related Resources

GGUF (FP16) Model Repo: SmallScale/Simple-Stories-Hindi-20M-GGUF
Live In-Browser Demo (WebGPU / WASM): SmallScale/simple-stories-20m-webgpu
Dataset: SmallScale/Simple-Stories-Hindi

📄 License

MIT License