Instructions to use glouriousgautam/LiLM1-230M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use glouriousgautam/LiLM1-230M with Transformers:

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

pipe = pipeline("text-generation", model="glouriousgautam/LiLM1-230M", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("glouriousgautam/LiLM1-230M", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use glouriousgautam/LiLM1-230M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "glouriousgautam/LiLM1-230M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "glouriousgautam/LiLM1-230M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/glouriousgautam/LiLM1-230M

SGLang

How to use glouriousgautam/LiLM1-230M 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 "glouriousgautam/LiLM1-230M" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "glouriousgautam/LiLM1-230M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "glouriousgautam/LiLM1-230M" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "glouriousgautam/LiLM1-230M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use glouriousgautam/LiLM1-230M with Docker Model Runner:
```
docker model run hf.co/glouriousgautam/LiLM1-230M
```

LiLM1-230M

LiLM1-230M is a compact language model for controlled tool use. It has 233,897,728 parameters and a 4,096-token context. A 4:8 general-to-tool mix worked best for this specific model.

IFEval, GSM8K, and TruthfulQA use the full suites. BBH and MMLU-Pro use fixed 50% samples.

Suite	LiLM1-230M	SmolLM2 135M Instruct	LFM2.5 230M	SmolLM2 360M Instruct
IFEval	15.5	21.8	65.8	32.2
GSM8K	1.8	1.4	29.0	9.3
TruthfulQA MC2	41.3	40.9	41.4	40.4
BBH	21.9	22.2	21.8	27.0
MMLU-Pro	8.4	6.3	7.1	8.8

Load

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "glouriousgautam/LiLM1-230M"
tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo,
    trust_remote_code=True,
    dtype=torch.bfloat16,
)

The architecture is custom. Loading requires trust_remote_code=True.

Use

Use a runtime controller for every tool call. The controller must validate the schema, required fields, permissions, execution, and result. Do not use the model for unsupervised consequential actions. Do not use context longer than 4,096 tokens.

Field	Value
Developer	Akshay Gautam
Parameters	233,897,728
Context	4,096 tokens
Pretraining	40B token exposures
Post-training mix	4 parts general data to 8 parts tool data
Tokenizer	SmolLM2 135M tokenizer
Source checkpoint SHA-256	`fe6f8dc5e340eead39e1825abc32474cc48636895e253df93635d74791f4dc0e`
Safetensors SHA-256	`ff0421a9ce25cafecd62861468b6a48a5f584e975e085f62d342a8cc7ac747f7`
License	Apache-2.0

License

Apache License 2.0.

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Safetensors

Model size

0.2B params

Tensor type

BF16

Collection including glouriousgautam/LiLM1-230M

LilM1

Collection

Collection of tiny models trained to be specially good at tool-use. Dataset and weights included. • 4 items • Updated 1 day ago • 1