Instructions to use RameshRathod/ego-45m-pretrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RameshRathod/ego-45m-pretrained with Transformers:

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

pipe = pipeline("text-generation", model="RameshRathod/ego-45m-pretrained")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RameshRathod/ego-45m-pretrained")
model = AutoModelForCausalLM.from_pretrained("RameshRathod/ego-45m-pretrained")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use RameshRathod/ego-45m-pretrained with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RameshRathod/ego-45m-pretrained"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RameshRathod/ego-45m-pretrained",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RameshRathod/ego-45m-pretrained

SGLang

How to use RameshRathod/ego-45m-pretrained 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 "RameshRathod/ego-45m-pretrained" \
    --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": "RameshRathod/ego-45m-pretrained",
		"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 "RameshRathod/ego-45m-pretrained" \
        --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": "RameshRathod/ego-45m-pretrained",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RameshRathod/ego-45m-pretrained with Docker Model Runner:
```
docker model run hf.co/RameshRathod/ego-45m-pretrained
```

Ego 45M — Pretrained Language Model

Model Summary

Ego 45M is a GPT-2 style decoder-only language model trained from scratch.

Key facts

Parameters: 44.9M
Context length: 1024 tokens
Tokenizer: GPT-2 BPE (tiktoken)
Hardware: NVIDIA H200
Data: FineWeb-Edu subset
Training tokens: ~392 million

Architecture

Decoder-only transformer
12 layers
12 attention heads
Embedding size: 768

How to use (Python)

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "RameshRathod/ego-45m-pretrained"
)
tokenizer = AutoTokenizer.from_pretrained("gpt2")

prompt = "Every effort moves you"
inputs = tokenizer(prompt, return_tensors="pt")

out = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(out[0]))


Updated model metadata (format: pt) at Sun Jan 18 01:03:59 IST 2026

Downloads last month: 3

Safetensors

Model size

70.6M params

Tensor type

F32

RameshRathod
/

ego-45m-pretrained

Ego 45M — Pretrained Language Model

Model Summary

Key facts

Architecture

How to use (Python)

Space using RameshRathod/ego-45m-pretrained 1