---
title: Llama-3.1-Nemotron-70B-Instruct
canonical_url: "https://www.modelscope.cn/models/unsloth/Llama-3.1-Nemotron-70B-Instruct"
md_url: "https://www.modelscope.cn/models/unsloth/Llama-3.1-Nemotron-70B-Instruct.md"
repository: unsloth/Llama-3.1-Nemotron-70B-Instruct
last_updated: 2024-11-13
license: llama3.1
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - llama
architectures:
  - LlamaForCausalLM
base_model:
  - nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
base_model_relation: finetune
parameters: 70.6B
tensor_type:
  - BF16
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
inference_backends:
  - "deploy_task text/emb"
  - "lmdeploy 0.9.1"
  - "lmdeploy_turbomind 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 1831
stars: 0
tags:
  - nvidia
  - llama3.1
  - unsloth
  - llama
---

# Llama-3.1-Nemotron-70B-Instruct

> Llama-3.1-Nemotron-70B-Instruct - unsloth 在 ModelScope 开源的模型。Finetune Llama 3.2, NVIDIA Nemotron, Mistral 2-5x faster with 70% less memory via Unsloth!

unsloth/Llama-3.1-Nemotron-70B-Instruct 是 ModelScope 魔搭社区上的 70.6B 参数text-generation模型，采用 llama3.1 许可，基于 nvidia/Llama-3.1-Nemotron-70B-Instruct-HF 构建，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: unsloth/Llama-3.1-Nemotron-70B-Instruct
- **License**: llama3.1
- **Tasks**: text-generation
- **Parameters**: 70.6B
- **Base model**: nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Tags**: nvidia, llama3.1, unsloth, llama
- **Downloads**: 1831
- **Stars**: 0
- **Last updated**: 2024-11-13

Source: https://www.modelscope.cn/models/unsloth/Llama-3.1-Nemotron-70B-Instruct

---

# Finetune Llama 3.2, NVIDIA Nemotron, Mistral 2-5x faster with 70% less memory via Unsloth!

We have a free Google Colab Tesla T4 notebook for Llama 3.2 (3B) here: https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/unsloth)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

# unsloth/Llama-3.1-Nemotron-70B-Instruct
For more details on the model, please go to NVIDIA's original [model card](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF)

## ✨ Finetune for Free

All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.

| Unsloth supports          |    Free Notebooks                                                                                           | Performance | Memory use |
|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
| **Llama-3.2 (3B)**      | [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing)               | 2.4x faster | 58% less |
| **Llama-3.1 (8B)**      | [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing)               | 2.4x faster | 58% less |
| **Phi-3.5 (mini)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1lN6hPQveB_mHSnTOYifygFcrO8C1bxq4?usp=sharing)               | 2x faster | 50% less |
| **Gemma 2 (9B)**      | [▶️ Start on Colab](https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing)               | 2.4x faster | 58% less |
| **Mistral (7B)**    | [▶️ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing)               | 2.2x faster | 62% less |
| **DPO - Zephyr**     | [▶️ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)               | 1.9x faster | 19% less |

- This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates.
- This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr.
- \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.

## Special Thanks
A huge thank you to the Meta and Llama team for creating these models and for NVIDIA fine-tuning them and releasing them.