[whatmodel.sh](../../README.md) / Llama 3.1 Tulu 3 70B DPO # Llama 3.1 Tulu 3 70B DPO Ai2 · released 2024-11 · 70.55B params · llama3.1 **score 54.2** ±1.1 · #299 of 732 · from 12 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 DPO among every model on that task or test, worst to best. 
task            vs the others          rank  counts  tests
chat & writing  ▁▁▁▃▆▄▆●▆▇▇▃   #237 of 402*    8.8%      2
math            ▁▁▃▅●▆▄█▄▂▁▁    #287 of 431     50%      4
chat & writing · #237 of 402 · 8.8% 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), [LFM2.5 1.2B Instruct](lfm2-5-1-2b-instruct.md), [Gemma 3 4B](gemma-3-4b.md) here, though they rank lower overall. 
test                                result  vs the others in its run    difficulty        counts  why
AlpacaEval 2 length-controlled (m…   49.6%  ▁▁▃▄▆██●▃▃▁▁ beats 127/155  medium · avg 43%    5.7%  sharp · at its level · small run
IFEval prompt-level loose (maker)    82.6%  ▁▂▁▂▁▃▁▂●▂▂▁ beats 16/25    easy · avg 78%      3.1%  small run
math · #287 of 431 · 50% of its score Beats [Molmo 2 8B](molmo-2-8b.md), [Llama 3.3 70B Instruct](llama-3-3-70b.md), [DeepSeek-V2.5](deepseek-v2-5.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), [Mistral 7B v0.1 (base)](mistral-7b-v0-1.md) here, though they rank lower overall. 
test                               result  vs the others in its run    difficulty            counts  why
MATH level 5                        42.7%  ▂▂▅▆▄●▆▃▅▂▃▄ beats 35/92    hard · avg 28%           29%  sharp · at its level
GSM8K (maker)                       93.5%  ▂▂▂▂▃▄▅▇██▆● beats 190/206  easy · avg 84%          8.4%  sharp · small run
MATH (subset unspecified) (maker)   62.3%  ▂▄▄▃█●▅▆▄▃▄▁ beats 36/80    medium · avg 54%        7.2%  at its level · small run
OTIS mock AIME 2024–2025             4.4%  ▃▃●▃▃▃▇▇█▃▂▅ beats 29/197   very hard · avg 7.2%    5.9%  sharp · noisy
other tests (6) 
test                    result  vs the others in its run    difficulty        counts  why
GPQA Diamond             46.3%  ▁▁▁▁▂▄●▄▅▇█▅ beats 54/211   hard · avg 39%       18%  sharp · at its level
BIG-Bench Hard (maker)   84.8%  ▁▁▁▁▂▄▅▆█▃●▁ beats 144/155  easy · avg 75%       10%  sharp · small run
MMLU (maker)             83.3%  ▁▁▁▁▁▂▂▅██●▄ beats 180/214  easy · avg 77%      8.0%  small run
PopQA (maker)            46.3%  ▁▂▁▃▃█▂█▆▅▁● beats 37/40    hard · avg 34%      3.0%  at its level · small run
DROP (maker)             74.1%  ▃▅█▅·▃·▆●▂▁▃ beats 37/44    medium · avg 62%    1.2%  blunt · at its level · small run
TruthfulQA MC2 (maker)   67.9%  █▇▇▄▂▄▁▆●▄▁▁ beats 70/79    medium · avg 59%    0.3%  blunt · at its level · small run
## 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
Epoch AI                                        3   53% ▇▇▇▇▇▇
Tülu 3: Pushing Frontiers in Open Langu…        9   47% ▇▇▇▇▇▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 54.2
Licences: Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Tülu 3: Pushing Frontiers in Open Language Model Post-Training ([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 DPO (Q4_K_M, needs ~42.5 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/Llama-3.1-Tulu-3-70B-DPO-GGUF/tree/467fa02d523d470e6eba3101b86c667ded5aa795)) ```sh llama cli -hf bartowski/Llama-3.1-Tulu-3-70B-DPO-GGUF:Q4_K_M ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf bartowski/Llama-3.1-Tulu-3-70B-DPO-GGUF:Q4_K_M ``` Other versions: Q8_0 (~75 GB) `llama cli -hf bartowski/Llama-3.1-Tulu-3-70B-DPO-GGUF:Q8_0` · IQ2_XXS (~19.1 GB) `llama cli -hf bartowski/Llama-3.1-Tulu-3-70B-DPO-GGUF:IQ2_XXS` ### 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 DPO (Q4_K_M, needs ~42.5 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/Llama-3.1-Tulu-3-70B-DPO-GGUF/tree/467fa02d523d470e6eba3101b86c667ded5aa795)) ```sh lms get https://huggingface.co/bartowski/Llama-3.1-Tulu-3-70B-DPO-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/bartowski/Llama-3.1-Tulu-3-70B-DPO-GGUF@Q8_0 -y` · IQ2_XXS (~19.1 GB) `lms get https://huggingface.co/bartowski/Llama-3.1-Tulu-3-70B-DPO-GGUF@IQ2_XXS -y` · 4bit (~39.7 GB, Mac only) `lms get https://huggingface.co/Scotto2025/Llama-3.1-Tulu-3-70B-DPO-Q4-mlx -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 DPO (Q4_K_M, needs ~42.5 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/Llama-3.1-Tulu-3-70B-DPO-GGUF/tree/467fa02d523d470e6eba3101b86c667ded5aa795)) ```sh ollama run hf.co/bartowski/Llama-3.1-Tulu-3-70B-DPO-GGUF:Q4_K_M ``` Other versions: IQ2_XXS (~19.1 GB) `ollama run hf.co/bartowski/Llama-3.1-Tulu-3-70B-DPO-GGUF:IQ2_XXS` ### 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 DPO (4bit, needs ~39.7 GB, Mac only, from [huggingface.co/Scotto2025](https://huggingface.co/Scotto2025/Llama-3.1-Tulu-3-70B-DPO-Q4-mlx/tree/b50472183075ec0f87938540fa63b7ebec5561af)) ```sh mlx_lm.chat --model Scotto2025/Llama-3.1-Tulu-3-70B-DPO-Q4-mlx ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model Scotto2025/Llama-3.1-Tulu-3-70B-DPO-Q4-mlx ``` ### Other ways to run it 1. Run it as published (BF16, needs ~169.82 GB, NVIDIA only, from [huggingface.co/allenai](https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B-DPO)) ```sh vllm serve allenai/Llama-3.1-Tulu-3-70B-DPO ``` Model repository and documentation: [huggingface.co/allenai](https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B-DPO) [interactive version](https://whatmodel.sh/model/llama-3-1-tulu-3-70b-dpo) · [JSON](https://whatmodel.sh/api?format=json&all=1)