[whatmodel.sh](../../README.md) / Llama-3-8B-ProLong-512k-Instruct # Llama-3-8B-ProLong-512k-Instruct Princeton NLP · released 2024-08 · 8.03B params · llama3 **score 50.8** ±4.2 · #353 of 732 · from 3 benchmark results across 2 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at _{● marks Llama-3-8B-ProLong-512k-Instruct among every model on that task or test, worst to best.}

other tests (3)

test                                result  vs the others in its run    difficulty        counts  why
Open PT-LLM Leaderboard average (…   68.8%  ▃▁▁▁▁▁▁▁▂●▅▁ beats 155/276  medium · avg 72%     42%  at its level · noisy
RULER 64k                            83.2%  ▁▁·▁·▂▃●█▂·▁ beats 10/22    easy · avg 82%       37%  typical
RULER 128k                           81.6%  ▄··▂▄▂▆██●▄▂ beats 18/21    easy · avg 67%       13%  at its level · noisy

## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md)

source                                    results  share of the score
NVIDIA RULER repository README results …        2   50% ▇▇▇▇▇▇
Open Portuguese LLM Leaderboard                 1   42% ▇▇▇▇▇
prior: pull toward the middle                        8% ▇
weighted average                                   = 50.8

_{Licences: NVIDIA RULER repository README results table ([Apache-2.0](https://github.com/NVIDIA/RULER/blob/c3f5e3b4f87f97e048793bb510a3a6b19a46bf3a/LICENSE)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt))} ## How to run it ### Other ways to run it 1. Run it as published (fp32, needs ~32.12 GB, from [huggingface.co/princeton-nlp](https://huggingface.co/princeton-nlp/Llama-3-8B-ProLong-512k-Instruct/tree/bf92e493b7b0ef1db0242bfa97f1d8f92be02e9c)) ```sh uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='princeton-nlp/Llama-3-8B-ProLong-512k-Instruct', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])" ``` Model repository and documentation: [huggingface.co/princeton-nlp](https://huggingface.co/princeton-nlp/Llama-3-8B-ProLong-512k-Instruct) [interactive version](https://whatmodel.sh/model/llama-3-8b-prolong-512k-instruct) · [JSON](https://whatmodel.sh/api?format=json&all=1)