Instructions to use sraivante/Custom-GPT-40M-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sraivante/Custom-GPT-40M-Base with Transformers:

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

pipe = pipeline("text-generation", model="sraivante/Custom-GPT-40M-Base")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sraivante/Custom-GPT-40M-Base")
model = AutoModelForCausalLM.from_pretrained("sraivante/Custom-GPT-40M-Base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sraivante/Custom-GPT-40M-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sraivante/Custom-GPT-40M-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sraivante/Custom-GPT-40M-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/sraivante/Custom-GPT-40M-Base

SGLang

How to use sraivante/Custom-GPT-40M-Base 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 "sraivante/Custom-GPT-40M-Base" \
    --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": "sraivante/Custom-GPT-40M-Base",
		"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 "sraivante/Custom-GPT-40M-Base" \
        --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": "sraivante/Custom-GPT-40M-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use sraivante/Custom-GPT-40M-Base with Docker Model Runner:
```
docker model run hf.co/sraivante/Custom-GPT-40M-Base
```

Custom-GPT-40M-Base / training_data /ATTRIBUTION.md

sraivante

Publish Custom GPT 40M base model with training data

87842ae verified 19 days ago

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	# Sangraha attribution and changes

	Corpus source: AI4Bharat, Sangraha (IndicLLMSuite).
	Source URL: https://huggingface.co/datasets/ai4bharat/sangraha
	Pinned revision: 8b813c3f62d37b2fa174d68c31e8b35ae2fe85e8
	Included source files: verified/eng/data-0.parquet and verified/eng/data-1.parquet.

	The source repository declares Creative Commons Attribution 4.0 International:
	https://creativecommons.org/licenses/by/4.0/
	The full license is preserved in LICENSE. Original content creators retain their rights.
	This release makes no claim that sraivante wrote the third-party source texts.

	Changes by sraivante / this release:
	- Selected two existing English shards for the project; raw bytes are unchanged.
	- Encoded the corpus with the project's custom 16,000-token byte-level BPE tokenizer,
	appended EOS after documents and split the token stream at 90% of total tokens.
	- Packaged the saved arrays, source identity, integrity hashes and original release documentation.
	No additional filtering, rewriting, redaction or deduplication was applied during publication.
	The tokenizer was fitted before the split; one document straddles the split.

	Please retain this attribution and cite the source authors' work:

	```bibtex
	@article{khan2024indicllmsuite,
	title = {IndicLLMSuite: A Blueprint for Creating Pre-training and Fine-Tuning Datasets for Indian Languages},
	author = {Mohammed Safi Ur Rahman Khan and Priyam Mehta and Ananth Sankar and Umashankar Kumaravelan and Sumanth Doddapaneni and Suriyaprasaad G and Varun Balan G and Sparsh Jain and Anoop Kunchukuttan and Pratyush Kumar and Raj Dabre and Mitesh M. Khapra},
	year = {2024},
	journal = {arXiv preprint arXiv: 2403.06350}
	}
	```

	Copyright (c) 2026 sraivante, for original code, release documentation and original
	packaging contributions only, where applicable, under Apache License 2.0.
	See LICENSE-APACHE-2.0. This grant does not replace the corpus's CC BY 4.0 terms.