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 17 days ago

preview code

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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:

@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.