Update README.md

f73ca2c verified 9 months ago

5.65 kB

datasets:
  - agentica-org/DeepScaleR-Preview-Dataset
language:
  - en
metrics:
  - accuracy
base_model:
  - nvidia/Llama-3.1-Nemotron-Nano-8B-v1

Model Overview

DLER-Llama-Nemotron-8B-Merge

🚀 The leading efficient reasoning model for cutting-edge research and development 🌟

Description:

DLER-Llama-3.1-Nemotron-8B is an ultra-efficient 8B open-weight reasoning model designed for challenging tasks such as mathematics, programming, and scientific problem-solving. It is first trained with the DLER algorithm on agentica-org/DeepScaleR-Preview-Dataset and then enhanced using a weight-merging technique to merge with the base model to mitigate accuracy degradation. Compared to the Llama-3.1-Nemotron-8B model, DLER-Llama-Nemotron-8B-Merge achieves substantial efficiency gains, reducing the average response length by nearly 50% across diverse mathematical benchmarks without sacrificing accuracy.

This model is for research and development only.

Deployment Geography:

Global

Use Case:

Researchers and developers can use this model to solve math, coding, and STEM questions.

Release Date:

Hugging Face 9/10/2025 via https://huggingface.co/nvidia/DLER-nvidia/DLER-Llama-Nemotron-8B-Merge

Model Architecture:

Architecture Type: Dense decoder-only Transformer model

Network Architecture: Llama-3.1-Nemotron-Nano-8B-v1

**This model was developed based on Llama-3.1-Nemotron-Nano-8B-v1

Software Integration:

Runtime Engine(s): Transformers

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Hopper

Preferred/Supported Operating System(s):

Linux

The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.

Model Version(s):

1.0

Training Dataset:

Dataset	Link
DeepScaleR-Preview-Dataset	Link

Properties: 479K question and answer pairs

Evaluation Results:

**Benchmark Score

Model	MATH	Length	AIME	Length	AMC	Length	Minerva	Length	Olympiad	Length	Total Avg
Llama-3.1-Nemotron-Nano-8B-v1	95.4	3069	66.4	9899	88.25	6228	52.38	4031	64.33	6755	5996
DLER-Llama-Nemotron-8B-Merge	95.2	1995	66.7	5013	89.23	3358	53.19	2301	65.39	3520	3237 (-46%)

Inference:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch


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


tokenizer = AutoTokenizer.from_pretrained("nvidia/DLER-Llama-Nemotron-8B-Merge-Research")
model = AutoModelForCausalLM.from_pretrained("nvidia/DLER-Llama-Nemotron-8B-Merge-Research").to(device)


messages = [{"role": "system", "content": "detailed thinking on"}, {"role": "user", "content": "Below is a math question. I want you to reason through the steps and then give a final answer. Your final answer should be in \\boxed{}.\nQuestion: Convert the point $(0,3)$ in rectangular coordinates to polar coordinates.  Enter your answer in the form $(r,\\theta),$ where $r > 0$ and $0 \\le \\theta < 2 \\pi.$"}]

tokenized_chat = tokenizer.apply_chat_template(
   messages,
   tokenize=True,
   add_generation_prompt=True,
   return_tensors="pt").to(model.device)


outputs = model.generate(
   tokenized_chat,
   max_new_tokens=10000,
   eos_token_id=tokenizer.eos_token_id)


print(tokenizer.decode(outputs[0], skip_special_tokens=False))

License/Terms of Use

NSCLv1

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Please report security vulnerabilities or NVIDIA AI Concerns here.

Citation

If you find our dataset helpful, please cite the following paper: