Instructions to use SmallScale/Simple-Stories-Hindi-10M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SmallScale/Simple-Stories-Hindi-10M with Transformers:

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

pipe = pipeline("text-generation", model="SmallScale/Simple-Stories-Hindi-10M", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("SmallScale/Simple-Stories-Hindi-10M", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SmallScale/Simple-Stories-Hindi-10M 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-10M"
# 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-10M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SmallScale/Simple-Stories-Hindi-10M

SGLang

How to use SmallScale/Simple-Stories-Hindi-10M 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-10M" \
    --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-10M",
		"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-10M" \
        --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-10M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SmallScale/Simple-Stories-Hindi-10M with Docker Model Runner:
```
docker model run hf.co/SmallScale/Simple-Stories-Hindi-10M
```

Simple-Stories-Hindi-10M / README.md

kaushik-harsh-99

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

a5cad98 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-10M
    results: []

📖 Simple-Stories-Hindi-10M (11.45M Parameters)

A 11.45M 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	`1.8157` (Cross-Entropy Loss)
Total Training Steps	202,000 steps
Total Parameters	11,453,120 (11.45M)
Non-Embedding Parameters	10,173,120 (10.17M)
Training Dataset	SmallScale/Simple-Stories-Hindi (~2.11M stories)
Model Size on Disk	~45.8 MB (`model.safetensors`)

🏗️ Model Architecture Details

Parameter	Value	Notes
Architecture	LLaMA-style Decoder	RoPE + SwiGLU + RMSNorm
Hidden Size (`d_model`)	320	Vector dimension
FFN Intermediate Size	896	8/3 × `d_model` rounded to multiple of 64
Layers (`n_layers`)	7	Transformer blocks
Attention Heads (`n_heads`)	5	Multi-Head Self Attention
Head Dimension	64	`d_model` / `n_heads`
Context Length (`max_seq_len`)	512 tokens	Sequence window
Vocabulary Size	4,000	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-10M", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("SmallScale/Simple-Stories-Hindi-10M", 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 मैं छाया से देखता हूं। मेरे दो लोग, जीन और सैमुअल हैं, जो एक भव्य यात्रा पर जा रहे हैं। वे एक ही स्थान पर रहते हैं, लेकिन वे दोनों अपनी-अपनी कहानियाँ चाहते हैं...

🔗 Related Resources

GGUF (FP16) Model Repo: SmallScale/Simple-Stories-Hindi-10M-GGUF
20M Model Repo: SmallScale/Simple-Stories-Hindi-20M
Dataset: SmallScale/Simple-Stories-Hindi

📄 License

MIT License