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

Libraries

How to use udgamlabs/Laghu-MLX with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("udgamlabs/Laghu-MLX")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use udgamlabs/Laghu-MLX with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "udgamlabs/Laghu-MLX"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "udgamlabs/Laghu-MLX"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use udgamlabs/Laghu-MLX with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "udgamlabs/Laghu-MLX"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "udgamlabs/Laghu-MLX"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "udgamlabs/Laghu-MLX",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use udgamlabs/Laghu-MLX with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "udgamlabs/Laghu-MLX"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default udgamlabs/Laghu-MLX

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use udgamlabs/Laghu-MLX with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "udgamlabs/Laghu-MLX"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "udgamlabs/Laghu-MLX" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

UDGAM LABS · MLX · bf16, 303.6 MB

Laghu-MLX

Laghu, a 152M-parameter English chat model trained from scratch by Udgam Labs for $146 of pretraining compute, converted to MLX (bf16) for Apple silicon Macs. Full precision; use Laghu-MLX-8bit for half the size at almost the same quality.

Try it: in your browser, on your device · code on GitHub · udgamlabs.com/models/laghu

Quick start

pip install mlx-lm
mlx_lm.generate --model udgamlabs/Laghu-MLX --prompt "Who are you?" \
  --chat-template-config '{"enable_thinking": false}'

For an OpenAI-compatible server: mlx_lm.server --model udgamlabs/Laghu-MLX --chat-template-args '{"enable_thinking":false}'. The MLX server turns thinking on by default; the switch above turns it off. Sampling defaults: temperature 0.7, top-p 0.95, top-k 64. Keep context at 16,384 tokens or less.

Results

Measured by us on 2026-10-22 on a Mac mini (Apple M6, 24 GB), mlx-lm 0.32.0:

File	Size	Perplexity	Mean KL vs F16	Top-1 agreement	Generation speed
Laghu-MLX (bf16)	303.6 MB	52.25	0.0009	98.0%	about 319 tokens/s

Held-out text: 32 windows of 2,048 tokens, compared with the F16 GGUF reference. MLX applies Laghu's 512-token sliding window, and the chat format (thinking off and on, multi-turn, tool calls) was checked in the MLX server. We do not publish a 4-bit MLX build: it loses clearly (mean KL 0.337, top-1 agreement 69.9%).

For the model's own results, see the Laghu model card.

Where it falls short

Laghu is a small research model: weak at maths (GSM8K 7.9% with thinking) and instruction following (IFEval 26.1), knows little, and can loop with greedy decoding. Details on the model card.
The MLX server turns thinking on by default; use the switch above for normal chat.

How it was trained

See the Laghu model card and the technical report. Converted from the released safetensors with mlx_lm.convert (mlx-lm 0.32.0).

Intended use and limitations

Local, offline use and research on Macs. Not for factual answers or decisions that matter; light safety training.

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