Instructions to use NousResearch/Yarn-Llama-2-70b-32k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NousResearch/Yarn-Llama-2-70b-32k with Transformers:

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

pipe = pipeline("text-generation", model="NousResearch/Yarn-Llama-2-70b-32k", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NousResearch/Yarn-Llama-2-70b-32k", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("NousResearch/Yarn-Llama-2-70b-32k", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use NousResearch/Yarn-Llama-2-70b-32k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NousResearch/Yarn-Llama-2-70b-32k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NousResearch/Yarn-Llama-2-70b-32k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/NousResearch/Yarn-Llama-2-70b-32k

SGLang

How to use NousResearch/Yarn-Llama-2-70b-32k 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 "NousResearch/Yarn-Llama-2-70b-32k" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NousResearch/Yarn-Llama-2-70b-32k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "NousResearch/Yarn-Llama-2-70b-32k" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NousResearch/Yarn-Llama-2-70b-32k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use NousResearch/Yarn-Llama-2-70b-32k with Docker Model Runner:
```
docker model run hf.co/NousResearch/Yarn-Llama-2-70b-32k
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card: Yarn-Llama-2-70b-32k

Preprint (arXiv)
GitHub

The authors would like to thank LAION AI for their support of compute for this model. It was trained on the JUWELS supercomputer.

Model Description

Nous-Yarn-Llama-2-70b-32k is a state-of-the-art language model for long context, further pretrained on long context data for 400 steps using the YaRN extension method. It is an extension of Llama-2-70b-hf and supports a 32k token context window.

To use, pass trust_remote_code=True when loading the model, for example

model = AutoModelForCausalLM.from_pretrained("NousResearch/Yarn-Llama-2-70b-32k",
  use_flash_attention_2=True,
  torch_dtype=torch.bfloat16,
  device_map="auto",
  trust_remote_code=True)

In addition you will need to use the latest version of transformers (until 4.35 comes out)

pip install git+https://github.com/huggingface/transformers

Benchmarks

Long context benchmarks:

Model	Context Window	1k PPL	2k PPL	4k PPL	8k PPL	16k PPL	32k PPL
Llama-2-70b-hf	4k	3.71	3.27	2.96	-	-	-
Yarn-Llama-2-70b-32k	32k	3.61	3.22	2.91	2.82	2.45	2.23

Short context benchmarks showing that quality degradation is minimal:

Model	Context Window	ARC-c	MMLU	Truthful QA
Llama-2-70b-hf	4k	67.32	69.83	44.92
Yarn-Llama-2-70b-32k	32k	67.41	68.84	46.14

Collaborators

bloc97: Methods, paper and evals
@theemozilla: Methods, paper, model training, and evals
@EnricoShippole: Model training
honglu2875: Paper and evals

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Quantizations

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Dataset used to train NousResearch/Yarn-Llama-2-70b-32k

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Collection including NousResearch/Yarn-Llama-2-70b-32k

YaRN

Collection

Extension of Llama 2 to 128k context windows • 17 items • Updated Sep 8, 2025 • 34

Paper for NousResearch/Yarn-Llama-2-70b-32k

YaRN: Efficient Context Window Extension of Large Language Models

Paper • 2309.00071 • Published Aug 31, 2023 • 87