Instructions to use sagar118/Custom-LLM-100M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sagar118/Custom-LLM-100M with Transformers:

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

pipe = pipeline("text-generation", model="sagar118/Custom-LLM-100M")

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

tokenizer = AutoTokenizer.from_pretrained("sagar118/Custom-LLM-100M")
model = AutoModelForCausalLM.from_pretrained("sagar118/Custom-LLM-100M", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sagar118/Custom-LLM-100M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sagar118/Custom-LLM-100M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sagar118/Custom-LLM-100M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/sagar118/Custom-LLM-100M

SGLang

How to use sagar118/Custom-LLM-100M 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 "sagar118/Custom-LLM-100M" \
    --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": "sagar118/Custom-LLM-100M",
		"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 "sagar118/Custom-LLM-100M" \
        --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": "sagar118/Custom-LLM-100M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use sagar118/Custom-LLM-100M with Docker Model Runner:
```
docker model run hf.co/sagar118/Custom-LLM-100M
```

Architecture

Decoder-only Transformer (GPT-style)
12 layers
Hidden size: 768
Attention heads: 12
Context length: 512
Parameters: ~100M

Training

Dataset: News articles (CNN/DailyMail – articles only)
Objective: Causal Language Modeling
Hardware: Google Colab GPU
Precision: FP16
Training steps: 2000
Optimizations: Gradient checkpointing, gradient accumulation

Training Loss Curve

The training loss decreased steadily from approximately 9.1 to 5.3 over 2000 training steps, indicating stable convergence during from-scratch training of the 100M-parameter language model.

Intended Use

Research
Educational purposes
Text generation experiments

Limitations

Not instruction-tuned
Trained for limited steps
Outputs may be verbose or repetitive

Downloads last month: 264

Safetensors

Model size

0.1B params

Tensor type

F32