[whatmodel.sh](../../README.md) / Llama 3.1 Tulu 3 70B
# Llama 3.1 Tulu 3 70B
Ai2 · released 2024-11 · 70.6B params · llama3.1
**score 54.8** ±1.4 · #285 of 732 · from 10 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 3.1 Tulu 3 70B among every model on that task or test, worst to best.
task vs the others rank counts tests
chat & writing ▁▁▁▃▆▄●█▆▇▇▃ #246 of 402 36% 3
math ▁▁▃▅●▆▄█▄▂▁▁ #266 of 431* 26% 2
chat & writing · #246 of 402 · 36% of its score
Beats [Mistral Small 4 (119B-A6.5B)](mistral-small-4.md), [Qwen2.5 Coder 32B Instruct](qwen2-5-coder-32b.md), [Ministral 3 14B Reasoning 2512](ministral-3-14b-reasoning.md) here, though they rank higher overall. Trails [Flan-T5 XXL](flan-t5-xxl.md), [Granite 4.0 H 1B](granite-4-0-h-1b.md), [LFM2.5 1.2B Instruct](lfm2-5-1-2b-instruct.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1286.1 ▁▁▁▂▃▃●▆▆▇█▃ beats 113/385 avg 1260 22% blunt
AlpacaEval 2 length-controlled (m… 49.8% ▁▁▃▄▆██●▃▃▁▁ beats 128/155 medium · avg 43% 10% sharp · at its level · small run
IFEval prompt-level loose (maker) 83.2% ▁▂▁▂▁▃▁▂●▂▂▁ beats 18/25 easy · avg 78% 5.1% small run
math · #266 of 431 · 26% of its score
Beats [GPT-4o (2024-11-20)](gpt-4o-2024-11-20.md), [GPT-4 Turbo (2024-04-09)](gpt-4-turbo-2024-04-09.md), [Llama 3.1 405B Instruct](llama-3-1-405b.md) here, though they rank higher overall. Trails [DeepSeek R1 Distill Qwen 1.5B](deepseek-r1-distill-qwen-1-5b.md), [Gemma 7B (base)](gemma-7b-base.md), [CodeGemma 7B (v1.0)](codegemma-7b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K (maker) 93.5% ▂▂▂▂▃▄▅▇██▆● beats 191/206 easy · avg 84% 14% sharp · small run
MATH (subset unspecified) (maker) 63.0% ▂▄▄▃█●▅▆▄▃▄▁ beats 37/80 medium · avg 54% 12% at its level · small run
other tests (5)
test result vs the others in its run difficulty counts why
BIG-Bench Hard (maker) 85.0% ▁▁▁▁▂▄▅▆█▃●▁ beats 145/155 easy · avg 75% 16% sharp · small run
MMLU (maker) 83.1% ▁▁▁▁▁▂▂▅██●▄ beats 178/214 easy · avg 77% 13% small run
PopQA (maker) 46.5% ▁▂▁▃▃█▂█▆▅▁● beats 38/40 hard · avg 34% 5.1% at its level · small run
DROP (maker) 74.3% ▃▅█▅·▃·▆●▂▁▃ beats 38/44 medium · avg 62% 1.9% blunt · at its level · small run
TruthfulQA MC2 (maker) 67.6% █▇▇▄▂▄▁▆●▄▁▁ beats 69/79 medium · avg 59% 0.6% blunt · at its level · small run
source results share of the score
Tülu 3: Pushing Frontiers in Open Langu… 9 77% ▇▇▇▇▇▇▇▇▇
LMArena leaderboard dataset 1 22% ▇▇▇
prior: pull toward the middle 1% ▇
weighted average = 54.8
Licences: Tülu 3: Pushing Frontiers in Open Language Model Post-Training ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))
## How to run it
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run Llama 3.1 Tulu 3 70B (Q4_K_M, needs ~42.5 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Llama-3.1-Tulu-3-70B-GGUF/tree/ddb937290558ba8fb8587c7c42c7ca041fc1aa2d))
```sh
llama cli -hf lmstudio-community/Llama-3.1-Tulu-3-70B-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf lmstudio-community/Llama-3.1-Tulu-3-70B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~75 GB) `llama cli -hf lmstudio-community/Llama-3.1-Tulu-3-70B-GGUF:Q8_0`
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run Llama 3.1 Tulu 3 70B (Q4_K_M, needs ~42.5 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Llama-3.1-Tulu-3-70B-GGUF/tree/ddb937290558ba8fb8587c7c42c7ca041fc1aa2d))
```sh
lms get https://huggingface.co/lmstudio-community/Llama-3.1-Tulu-3-70B-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~75 GB) `lms get https://huggingface.co/lmstudio-community/Llama-3.1-Tulu-3-70B-GGUF@Q8_0 -y` · 4bit (~39.7 GB, Mac only) `lms get https://huggingface.co/mlx-community/Llama-3.1-Tulu-3-70B-4bit -y`
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run Llama 3.1 Tulu 3 70B (Q4_K_M, needs ~42.86 GB, from [ollama.com](https://ollama.com/library/tulu3/tags))
```sh
ollama run tulu3:70b-q4_K_M
```
Other versions: Q8_0 (~85.22 GB) `ollama run tulu3:70b-q8_0`
### MLX (Mac)
1. Install MLX (Mac). Apple silicon Macs only, text only.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run Llama 3.1 Tulu 3 70B (4bit, needs ~39.7 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Llama-3.1-Tulu-3-70B-4bit/tree/6a2fcbb258f7b01f0a9885f319cee5c9813e4738))
```sh
mlx_lm.chat --model mlx-community/Llama-3.1-Tulu-3-70B-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Llama-3.1-Tulu-3-70B-4bit
```
Model repository and documentation: [huggingface.co/allenai](https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B)
[interactive version](https://whatmodel.sh/model/llama-3-1-tulu-3-70b) · [JSON](https://whatmodel.sh/api?format=json&all=1)