Instructions to use FrontiersMind/Lumma-0.6B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FrontiersMind/Lumma-0.6B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="FrontiersMind/Lumma-0.6B-Instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("FrontiersMind/Lumma-0.6B-Instruct", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FrontiersMind/Lumma-0.6B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FrontiersMind/Lumma-0.6B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FrontiersMind/Lumma-0.6B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FrontiersMind/Lumma-0.6B-Instruct

SGLang

How to use FrontiersMind/Lumma-0.6B-Instruct 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 "FrontiersMind/Lumma-0.6B-Instruct" \
    --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": "FrontiersMind/Lumma-0.6B-Instruct",
		"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 "FrontiersMind/Lumma-0.6B-Instruct" \
        --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": "FrontiersMind/Lumma-0.6B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FrontiersMind/Lumma-0.6B-Instruct with Docker Model Runner:
```
docker model run hf.co/FrontiersMind/Lumma-0.6B-Instruct
```

Lumma-0.6B-Instruct / README.md

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	---
	license: apache-2.0
	language:
	- en
	- hi
	- mr
	- ta
	- te
	- kn
	- ml
	- bn
	- pa
	- gu
	- or
	pipeline_tag: text-generation
	library_name: transformers
	base_model:
	- FrontiersMind/Lumma-0.6B-Base
	---

	# Lumma-0.6B-Instruct

	## Introduction

	Lumma-0.6B-Instruct is a compact, efficient multilingual language model designed for strong performance in resource-constrained environments. It is pre-trained from scratch on 1 trillion tokens and further enhanced through instruction tuning and Direct Preference Optimisation. This is a pre-RL checkpoint. The model supports English and 10 Indic languages.

	### Benchmark results

	<img src="benchmark_image.jpg" width="1500"/>

	We benchmarked Lumma-0.6B-Instruct across multiple benchmarks, with an intentional focus on instruction-following capabilities. Despite its compact size, Lumma-0.6B-Instruct is able to match or outperform similar models up to 3× larger on several instruction-following benchmarks.

	While the model also delivers decent performance on mathematics and coding, we believe these capabilities are less critical for the primary real-world use cases targeted by such a small model, where developers typically prioritize efficient and reliable instruction following.

	We expect further improvements with the RL-trained version of Lumma-0.6B-Instruct, particularly as we continue optimizing the model for real-world instruction-following tasks.

	## 🌍 Supported Languages

	The model is trained on English and a diverse set of Indic languages, including Hindi, Bengali, Tamil, Telugu, Marathi, Gujarati, Kannada, Malayalam, Punjabi, Odia


	## 🚀 Usage

	```python
	!pip install transformers=='5.4.0'

	from IPython.display import display, Markdown
	from transformers import AutoTokenizer, AutoModelForCausalLM
	import torch

	model_name = "FrontiersMind/Lumma-0.6B-Instruct"

	device = "cuda" if torch.cuda.is_available() else "cpu"

	tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
	model = AutoModelForCausalLM.from_pretrained(
	model_name,
	trust_remote_code=True,
	dtype=torch.bfloat16
	).to(device).eval()

	prompt = "Explain newton's second law of motion"

	messages = [
	{"role": "user", "content": prompt}
	]

	prompt = tokenizer.apply_chat_template(messages, tokenize=False)
	inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

	generated_ids = model.generate(
	**inputs,
	max_new_tokens=500,
	do_sample=True,
	temperature=0.3,
	top_p=0.90,
	top_k=20,
	repetition_penalty=1.1,
	)

	generated_ids = [
	output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, generated_ids)
	]

	response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

	#print(response)
	Markdown(response)
	```



	## 📬 Feedback & Suggestions

	We’d love to hear your thoughts, feedback, and ideas!

	- Discord: https://discord.gg/ZGdjCdRt
	- Email: support@frontiersmind.ai
	- Official Website https://www.frontiersmind.ai/
	- LinkedIn: https://www.linkedin.com/company/frontiersmind/
	- X (Twitter): https://x.com/FrontiersMind