Instructions to use udgamlabs/Laghu-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use udgamlabs/Laghu-Base with Transformers:

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

pipe = pipeline("text-generation", model="udgamlabs/Laghu-Base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

tokenizer = AutoTokenizer.from_pretrained("udgamlabs/Laghu-Base")
model = AutoModelForCausalLM.from_pretrained("udgamlabs/Laghu-Base", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use udgamlabs/Laghu-Base with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/udgamlabs/Laghu-Base

SGLang

How to use udgamlabs/Laghu-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 "udgamlabs/Laghu-Base" \
    --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": "udgamlabs/Laghu-Base",
		"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 "udgamlabs/Laghu-Base" \
        --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": "udgamlabs/Laghu-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use udgamlabs/Laghu-Base with Docker Model Runner:
```
docker model run hf.co/udgamlabs/Laghu-Base
```

UDGAM LABS · Language model · 152M parameters, base

Laghu-Base

Laghu-Base is the pretrained base model behind Laghu: 152M parameters, trained from scratch on 40.0B token positions of English web, educational, maths, code and synthetic text at a 2,048-token context. These are the exponential-moving-average (EMA) weights at the last step, 76,294. On text published in September and October 2026 it scores 0.919 bits per byte, better than SmolLM2-135M (0.941, pretrained on 2T tokens) and Falcon-H1-Tiny-90M-Base (0.979), after a $146 pretraining run. It is a text completer, not a chat model. Part of a fully open research release: code, recipe, checkpoints, evaluations and cost are all public.

Try it: the chat model · code on GitHub · udgamlabs.com/models/laghu

Quick start

from transformers import AutoTokenizer, AutoModelForCausalLM

tok = AutoTokenizer.from_pretrained("udgamlabs/Laghu-Base")
model = AutoModelForCausalLM.from_pretrained("udgamlabs/Laghu-Base")
inputs = tok("The water cycle begins when", return_tensors="pt")   # the tokenizer adds <bos>
out = model.generate(**inputs, max_new_tokens=60, do_sample=True, temperature=0.8, top_p=0.95)
print(tok.decode(out[0], skip_special_tokens=True))

Keep prompts and outputs within 2,048 tokens: the model was trained at that length and fails at longer ones (the config allows 8,192 positions, but recall at 4K is 0%). For long inputs use Laghu-Base-32K. The raw (non-EMA) weights of the same step are in the raw-final/ folder: AutoModelForCausalLM.from_pretrained("udgamlabs/Laghu-Base", subfolder="raw-final").

Results

Zero-shot benchmarks (lm-eval 0.4.13, bf16, <bos> added, CUDA; measured by us for every model, 2026-10-19):

Model	Params	ARC-E	ARC-C	HellaSwag	PIQA	WinoGrande	5-task avg	MMLU	GSM8K 5-shot
Laghu-Base	152M	60.6	30.1	40.9	65.7	52.2	49.9	25.3	4.2
GPT-X3-150M	149M	63.0	31.4	45.9	71.3	53.6	53.0	24.6	4.2
SmolLM2-135M	135M	64.5	30.0	43.0	68.3	53.0	51.8	24.5	2.0
Falcon-H1-Tiny-90M-Base	91M	54.3	28.5	37.7	64.8	50.8	47.2	32.4	2.7
Gargi-Base (ours)	384M	63.0	32.5	41.5	66.9	54.5	51.7	25.3	5.7

ARC-C, HellaSwag and PIQA use length-normalised accuracy. Laghu-Base is 5th of the 19 models up to 200M parameters we measured on 19 October on the 5-task average; the full table is in the technical report.

Bits per byte on held-out text (lower is better; one script and the same documents for every model):

Model	New text (1,695 docs: Wikipedia pages created and arXiv abstracts submitted Sep-Oct 2026)	Web (580)	Reasoning (1,179)
Laghu-Base	0.919	0.858	0.693
SmolLM2-135M	0.941	0.858	0.752
Falcon-H1-Tiny-90M-Base	0.979	0.910	0.769
Gargi-Base (ours, 384M)	0.914	0.850	0.686
SmolLM2-360M	0.850	0.788	0.678

Where it falls short

Below SmolLM2-135M (51.8) and GPT-X3-150M (53.0) on the commonsense average; near chance on MMLU (25.3).
Slightly behind our 384M Gargi-Base on all three held-out text sets (the differences are significant).
Our scaling projection before the run expected 0.900-0.912 bits per byte on new text after a 32K stretch; the measured value was 0.921, so the run came in worse than planned.
2,048-token context only.
Not instruction-tuned: it continues text and does not follow requests.

How it was trained

Data: a 52.6B-token pool from 14 public sources (DCLM-Edu and FineWeb-Edu ≈ 35%, FinePhrase ≈ 23%, Ultra-FineWeb ≈ 14.5%, FineMath ≈ 9%, SYNTH ≈ 8.5%, Wikipedia ≈ 3.4%, NVIDIA Nemotron chat and STEM ≈ 5%, permissive GitHub code ≈ 1.7%), 13-gram decontaminated against 10 evaluation sets (21,427 documents removed). Shares are the builder's own token estimates.
Run: 76,294 steps × 524,288 tokens = 40.0B token positions; Muon; token bags (4 tokens per position) for the first 25% of steps, so about 70B tokens were read; cool-down data mix from 80%; learning rate warm-down over the last 65% to 5% of peak; EMA from 90% (β 0.998); bf16.
Compute and cost: 8× A100 80GB for 13.2 hours, $146.26. About 3.6 × 10¹⁹ FLOPs.
Architecture: Gemma-4 text layout, 28 layers, width 640, 10 query / 2 key-value heads, 512-token sliding window with every 4th layer global, tied embeddings, our own 32,768-token tokenizer.

Every setting, the data builder and the launch scripts: GitHub. The raw training states with optimiser at steps 76,000 and 76,294 are in Laghu-Checkpoints; the checkpoints from steps 2,000-74,000 were deleted by a storage clean-up on 2026-10-21, our mistake.

Intended use and limitations

A base for research: fine-tuning, continued pretraining, interpretability and training-dynamics studies. It has no safety tuning and continues any text, including harmful or false text.

Licence

Citation

@misc{udgamlabs2026laghu,
  title  = {Laghu: a fully open 152M chat model trained from scratch},
  author = {Prasad, Jitesh K},
  year   = {2026},
  howpublished = {Udgam Labs},
  url    = {https://huggingface.co/udgamlabs/Laghu}
}

About Udgam Labs. Udgam Labs is an independent AI research lab. We work from first principles: before we build, we understand why things work, whether we are training a model, fine-tuning one or applying AI to a new problem. Udgam (उद्गम) is Sanskrit for origin. udgamlabs.com