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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	---
	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](https://huggingface.co/datasets/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

	```python
	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](https://huggingface.co/SmallScale/Simple-Stories-Hindi-20M-GGUF)
	- Live In-Browser Demo (WebGPU / WASM): [SmallScale/simple-stories-20m-webgpu](https://huggingface.co/spaces/SmallScale/simple-stories-20m-webgpu)
	- Dataset: [SmallScale/Simple-Stories-Hindi](https://huggingface.co/datasets/SmallScale/Simple-Stories-Hindi)

	---

	## 📄 License

	MIT License