Instructions to use princeton-nlp/Llama-3-8B-ProLong-64k-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use princeton-nlp/Llama-3-8B-ProLong-64k-Base with Transformers:

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

pipe = pipeline("text-generation", model="princeton-nlp/Llama-3-8B-ProLong-64k-Base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("princeton-nlp/Llama-3-8B-ProLong-64k-Base")
model = AutoModelForCausalLM.from_pretrained("princeton-nlp/Llama-3-8B-ProLong-64k-Base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use princeton-nlp/Llama-3-8B-ProLong-64k-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "princeton-nlp/Llama-3-8B-ProLong-64k-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "princeton-nlp/Llama-3-8B-ProLong-64k-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/princeton-nlp/Llama-3-8B-ProLong-64k-Base

SGLang

How to use princeton-nlp/Llama-3-8B-ProLong-64k-Base 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 "princeton-nlp/Llama-3-8B-ProLong-64k-Base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "princeton-nlp/Llama-3-8B-ProLong-64k-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "princeton-nlp/Llama-3-8B-ProLong-64k-Base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "princeton-nlp/Llama-3-8B-ProLong-64k-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use princeton-nlp/Llama-3-8B-ProLong-64k-Base with Docker Model Runner:
```
docker model run hf.co/princeton-nlp/Llama-3-8B-ProLong-64k-Base
```

Llama-3-8B-ProLong-64k-Base / README.md

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	---
	license: llama3
	datasets:
	- princeton-nlp/prolong-data-64K
	base_model:
	- meta-llama/Meta-Llama-3-8B-Instruct
	---

	# princeton_nlp/Llama-3-8B-ProLong-64k-Base

	[[Paper](https://arxiv.org/pdf/2410.02660)] [[HF Collection](https://huggingface.co/collections/princeton-nlp/prolong-66c72d55d2051a86ac7bd7e4)] [[Code](https://github.com/princeton-nlp/ProLong)]


	ProLong (<u>Pr</u>incet<u>o</u>n <u>long</u>-context language models) is a family of long-context models that are continued trained and supervised fine-tuned from Llama-3-8B, with a maximum context window of 512K tokens. Our [main ProLong model](https://huggingface.co/princeton-nlp/Llama-3-8B-ProLong-512k-Instruct) is one of the best-performing long-context models at the 10B scale (evaluated by [HELMET](https://github.com/princeton-nlp/helmet)).

	To train this strong long-context model, we conduct thorough ablations on the long-context pre-training data, SFT data, and numerous other design choices. We demonstrate our findings in our paper, [How to Train Long-Context Language Models (Effectively)](https://arxiv.org/pdf/2410.02660).


	Authors: [Tianyu Gao](https://gaotianyu.xyz/about)\, [Alexander Wettig](https://www.cs.princeton.edu/~awettig/)\, [Howard Yen](https://howard-yen.github.io/), [Danqi Chen](https://www.cs.princeton.edu/~danqic/) (* equal contribution)

	Contact: `{tianyug, awettig}@princeton.edu`

	## The ProLong Models

	- [princeton_nlp/Llama-3-8B-ProLong-64k-Base](https://huggingface.co/princeton-nlp/Llama-3-8B-ProLong-64k-Base) ← you are here!
	- [princeton_nlp/Llama-3-8B-ProLong-64k-Instruct](https://huggingface.co/princeton-nlp/Llama-3-8B-ProLong-64k-Instruct)
	- [princeton_nlp/Llama-3-8B-ProLong-512k-Base](https://huggingface.co/princeton-nlp/Llama-3-8B-ProLong-512k-Base)
	- ⭐ [princeton_nlp/Llama-3-8B-ProLong-512k-Instruct](https://huggingface.co/princeton-nlp/Llama-3-8B-ProLong-512k-Instruct)

	## Model card

	Here are some quick facts about our main ProLong model: [princeton-nlp/Llama-3-8B-ProLong-512k-Instruct](https://huggingface.co/princeton-nlp/Llama-3-8B-ProLong-512k-Instruct).
	* Base model: [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
	* Long-context continued training: 20B tokens on 64K training data ([princeton-nlp/prolong-data-64K](https://huggingface.co/datasets/princeton-nlp/prolong-data-64K)), and 20B tokens on 512K training data ([princeton-nlp/prolong-data-512K](https://huggingface.co/datasets/princeton-nlp/prolong-data-512K))
	* Supervised fine-tuning (SFT): [UltraChat](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k)
	* Maximum context window: 512K tokens


	<p align="center" style="margin-bottom: 0;">
	<img width="80%" alt="image" src="https://github.com/user-attachments/assets/c31c9671-49fe-4776-91d2-de70ffd9f9a1">
	</p>
	<p align="center" style="margin-top: 0; padding-top: 0;">
	<em>ProLong performance on <a href="https://github.com/princeton-nlp/helmet">HELMET</a> averaged over 32K, 64K, and 128K lengths. All models are instruct models.</em>
	</p>


	<p align="center">
	<img width="80%" alt="image" src="https://github.com/user-attachments/assets/a36a7d0f-4480-4a29-80f3-208477707fb7">
	</p>
	<p align="center" style="margin-top: 0;">
	<em>ProLong training recipe.</em>
	</p>


	## Citation

	```bibtex
	@article{gao2024prolong,
	title={How to Train Long-Context Language Models (Effectively)},
	author={Gao, Tianyu and Wettig, Alexander and Yen, Howard and Chen, Danqi},
	journal={arXiv preprint arXiv:2410.02660},
	year={2024}
	}
	```