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
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
!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