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Llama 3.1 Nemotron 70B Instruct HF by nvidia

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  Arxiv:2405.01481   Arxiv:2406.08673   Arxiv:2410.01257   Autotrain compatible Base model:finetune:meta-llama... Base model:meta-llama/llama-3....   Conversational   Dataset:nvidia/helpsteer2   En   Instruct   Llama   Llama3.1   Nvidia   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF 

Llama 3.1 Nemotron 70B Instruct HF Benchmarks

MMLU Pro: 43.54
GPQA: 1.12
MUSR: 13.2
BBH: 47.11
IFEval: 73.81 vs 88 (so35)-16.1%
MATH Lvl 5: 28.7
LLME Score: 0.64791
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Llama 3.1 Nemotron 70B Instruct HF (nvidia/Llama-3.1-Nemotron-70B-Instruct-HF)

Llama 3.1 Nemotron 70B Instruct HF Parameters and Internals

Model Type 
text generation
Use Cases 
Areas:
research, commercial applications
Applications:
general-domain instruction following, text-based query responses
Primary Use Cases:
improving helpfulness in language model responses
Limitations:
not tuned for math, unknown specialized domains
Additional Notes 
Evaluation results might differ from other platforms like NeMo-Aligner.
Supported Languages 
English (Fluent)
Training Details 
Data Sources:
nvidia/HelpSteer2
Data Volume:
21,362 prompt-responses
Methodology:
Reinforcement Learning with Human Feedback (RLHF) using REINFORCE
Context Length:
128000
Hardware Used:
H100, A100 80GB, A100 40GB
Model Architecture:
Llama 3.1 Transformer
Input Output 
Input Format:
String
Accepted Modalities:
text
Output Format:
String
Performance Tips:
Use at least 2 NVIDIA Ampere 80GB GPUs for optimal performance.
Release Notes 
Version:
v1.0
Date:
2024-10-01
Notes:
Achieved top performance on Arena Hard, AlpacaEval 2 LC, and MT Bench.
LLM NameLlama 3.1 Nemotron 70B Instruct HF
Repository 🤗https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF 
Base Model(s)  meta-llama/Llama-3.1-70B-Instruct   meta-llama/Llama-3.1-70B-Instruct
Model Size70b
Required VRAM141.9 GB
Updated2024-12-22
Maintainernvidia
Model Typellama
Instruction-BasedYes
Model Files  4.6 GB: 1-of-30   4.7 GB: 2-of-30   5.0 GB: 3-of-30   5.0 GB: 4-of-30   4.7 GB: 5-of-30   4.7 GB: 6-of-30   4.7 GB: 7-of-30   5.0 GB: 8-of-30   5.0 GB: 9-of-30   4.7 GB: 10-of-30   4.7 GB: 11-of-30   4.7 GB: 12-of-30   5.0 GB: 13-of-30   5.0 GB: 14-of-30   4.7 GB: 15-of-30   4.7 GB: 16-of-30   4.7 GB: 17-of-30   5.0 GB: 18-of-30   5.0 GB: 19-of-30   4.7 GB: 20-of-30   4.7 GB: 21-of-30   4.7 GB: 22-of-30   5.0 GB: 23-of-30   5.0 GB: 24-of-30   4.7 GB: 25-of-30   4.7 GB: 26-of-30   4.7 GB: 27-of-30   5.0 GB: 28-of-30   5.0 GB: 29-of-30   2.1 GB: 30-of-30
Supported Languagesen
Model ArchitectureLlamaForCausalLM
Licensellama3.1
Context Length131072
Model Max Length131072
Transformers Version4.40.0
Tokenizer ClassPreTrainedTokenizerFast
Vocabulary Size128256
Torch Data Typebfloat16

Quantized Models of the Llama 3.1 Nemotron 70B Instruct HF

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VRAM
... Nemotron 70B Instruct HF 4bit115629339 GB
...Nemotron 70B Instruct Bnb 4bit18228539 GB
... Nemotron 70B Instruct HF 4bit26739 GB

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Rank the Llama 3.1 Nemotron 70B Instruct HF Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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