Instructions to use 4lph4v3rs3/lightweightl-LLM-135M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 4lph4v3rs3/lightweightl-LLM-135M with Transformers:

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

pipe = pipeline("text-generation", model="4lph4v3rs3/lightweightl-LLM-135M")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("4lph4v3rs3/lightweightl-LLM-135M")
model = AutoModelForCausalLM.from_pretrained("4lph4v3rs3/lightweightl-LLM-135M")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use 4lph4v3rs3/lightweightl-LLM-135M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "4lph4v3rs3/lightweightl-LLM-135M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "4lph4v3rs3/lightweightl-LLM-135M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/4lph4v3rs3/lightweightl-LLM-135M

SGLang

How to use 4lph4v3rs3/lightweightl-LLM-135M 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 "4lph4v3rs3/lightweightl-LLM-135M" \
    --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": "4lph4v3rs3/lightweightl-LLM-135M",
		"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 "4lph4v3rs3/lightweightl-LLM-135M" \
        --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": "4lph4v3rs3/lightweightl-LLM-135M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use 4lph4v3rs3/lightweightl-LLM-135M with Docker Model Runner:
```
docker model run hf.co/4lph4v3rs3/lightweightl-LLM-135M
```

LightweightLLM-135M

Model Summary
How to Use
Evaluation
Limitations
Training
License
Citation

Model Summary

LightweightLLM-135M is a compact, efficient language model designed for on-device usage and low-resource environments. It is derived from the SmolLM2 family, providing strong instruction-following and reasoning capabilities in a lightweight format.

Parameters: 135M
Use Cases: Text generation, summarization, rewriting, basic reasoning
Language: English

How to Use

Installation

pip install transformers

Example Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

checkpoint = "4lph4v3rs3/lightweightl-LLM-135M"
device = "cuda"  # or "cpu"

tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)

inputs = tokenizer.encode("Gravity is", return_tensors="pt").to(device)
outputs = model.generate(inputs)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Using bfloat16

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

checkpoint = "4lph4v3rs3/lightweightl-LLM-135M"

tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(
    checkpoint,
    device_map="auto",
    torch_dtype=torch.bfloat16
)

inputs = tokenizer.encode("Gravity is", return_tensors="pt").to("cuda")
outputs = model.generate(inputs)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Evaluation

LightweightLLM-135M has been benchmarked on common NLP tasks using zero-shot evaluation, showing strong performance for a 135M-parameter model in instruction following, commonsense reasoning, and text generation.

Limitations

Only supports English
May produce incorrect or biased content
Not suitable as a definitive knowledge source

Use outputs as an assistive tool and verify important information independently.

Training

Architecture: Transformer decoder
Pretraining tokens: Large-scale text and code datasets
Precision: bfloat16
Framework: Hugging Face Nanotron

License

Apache 2.0

Citation

@misc{allal2025smollm2smolgoesbig,
  title={SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model},
  author={Loubna Ben Allal et al.},
  year={2025},
  eprint={2502.02737},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}

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Paper for 4lph4v3rs3/lightweightl-LLM-135M

SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model

Paper • 2502.02737 • Published Feb 4, 2025 • 261

4lph4v3rs3
/

lightweightl-LLM-135M