[whatmodel.sh](../../README.md) / Tulu 2 DPO 70B
# Tulu 2 DPO 70B
Ai2 · released 2023-11 · 69B params · ai2-impact-low-risk
**score 44.1** ±0.9 · #494 of 732 · from 12 benchmark results across 7 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Tulu 2 DPO 70B among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃●▇█▇▅▄▃▁ #385 of 469* 4.8% 1
chat & writing ▁▁▁▃●▄▆█▆▇▇▃ #325 of 402 58% 5
math ▁▁▃▅█●▄█▄▂▁▁ #217 of 431* 6.4% 1
coding · #385 of 469 · 4.8% of its score
Beats [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md), [DeepSeek R1 Distill Qwen 7B](deepseek-r1-distill-qwen-7b.md), [Tulu 2 70B](tulu-2-70b.md) here, though they rank higher overall. Trails [Qwen2.5 1.5B Instruct](qwen2-5-1-5b.md), [WizardLM 13B V1.2](wizardlm-13b-v1-2.md), [Falcon3 3B Instruct](falcon3-3b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Tulu 2: CodexEval pass@10 (maker) 68.9% ▃▃▃·▆▃▃●▃▂▂▅ beats 16/25 easy · avg 83% 4.8% sharp · at its level
chat & writing · #325 of 402 · 58% of its score
Beats [Gemma 4 E4B](gemma-4-e4b.md), [OpenThinker3 7B](openthinker3-7b.md), [Code Llama 70B Instruct](codellama-70b-instruct.md) here, though they rank higher overall. Trails [LFM2 350M](lfm2-350m.md), [Gemma 3 1B](gemma-3-1b.md), [Qwen3 0.6B](qwen3-0-6b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AlpacaEval 2 length-controlled 21.2% ▁▂▂▃▆▆●▆▄▄▁▁ beats 55/122 medium · avg 35% 25% sharp
Arena-Hard (v0.1, GPT-4-1106 judg… 15.0% ▂▁▁●█▅▄▃▃▅▃▁ beats 8/60 medium · avg 47% 15% sharp
LMArena (text) 1177.7 ▁▁▁▂●▃▃▆▆▇█▃ beats 54/385 avg 1260 8.5% blunt
Polyrating rating (bias-aware re-… 1122.0 ▂▁▂▁▃▇█●▇▄▂▂ beats 57/95 avg 1187 7.8% typical
AlpacaEval 1.0 win rate (vs text-… 95.0% ▂▂·▆·▇▃▆█▆●▂ beats 42/45 very easy · avg 94% 1.1% sharp · noisy
math · #217 of 431 · 6.4% of its score
Beats [MiniMax-M2.1](minimax-m2-1.md), [DeepSeek V3 (Dec '24)](deepseek-v3.md), [GPT-4o (2024-08-06)](gpt-4o-2024-08-06.md) here, though they rank higher overall. Trails [Granite 8B Code Base (4K)](granite-8b-code-base-4k.md), [Mixtral 8x7B v0.1 (base)](mixtral-8x7b-v0-1.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K (maker) 71.5% ▆▂▅█▅▅▃●···▂ beats 21/25 easy · avg 72% 6.4% sharp · at its level
other tests (5)
test result vs the others in its run difficulty counts why
BIG-Bench Hard (maker) 66.0% ▁·····▂█▂●·▁ beats 21/25 easy · avg 74% 12% sharp · at its level
WildBench v2 WB-Score (task macro… 27.9838 WB-Score ▁·▁▁·▃●▄▇▆▅▆ beats 10/52 avg 43.019 WB-Score 9.1% sharp
MMLU (maker) 67.8% ▁▁▃▃▄█▄▁●··▁ beats 23/25 medium · avg 73% 5.5% at its level
Open PT-LLM Leaderboard average (… 74.1% ▃▁▁▁▁▁▁▁▂█●▁ beats 213/276 medium · avg 72% 2.0% at its level · noisy
Tulu 2: TyDiQA gold passage, 1-sh… 35.8% ▂··▁●▂▂▂█▅▁▂ beats 3/25 medium · avg 59% 1.3% blunt · at its level
source results share of the score
Tulu 2 (submitted / maker-reported) 5 30% ▇▇▇▇
AlpacaEval 1.0 and 2.0 leaderboards 2 27% ▇▇▇
Arena-Hard-Auto v0.1 leaderboard 1 15% ▇▇
WildBench v2 leaderboard data (submitte… 1 9% ▇
LMArena leaderboard dataset 1 9% ▇
Polyrating: A Cost-Effective and Bias-A… 1 8% ▇
Open Portuguese LLM Leaderboard 1 2% ▇
prior: pull toward the middle 0% ▇
weighted average = 44.1
Licences: Tulu 2 ([CC BY 4.0](../../licenses/CC-BY-4.0.txt)) · AlpacaEval 1.0 and 2.0 leaderboards ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · Arena-Hard-Auto v0.1 leaderboard ([Apache-2.0](../../licenses/arena-hard-auto-v0-1-Apache-2.0.txt)) · WildBench v2 leaderboard data ([Apache-2.0](https://github.com/allenai/WildBench/blob/d6b8dcaf377d173d031980f97c16e1a82618c03d/LICENSE)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt))
## 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 Tulu 2 DPO 70B (Q4_K_M, needs ~41.4 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/tulu-2-dpo-70B-GGUF/tree/0f390651b9c7930543c3cca150881d028e115338))
```sh
llama cli -hf TheBloke/tulu-2-dpo-70B-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf TheBloke/tulu-2-dpo-70B-GGUF:Q4_K_M
```
Other versions: Q2_K (~29.3 GB) `llama cli -hf TheBloke/tulu-2-dpo-70B-GGUF:Q2_K`
### 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 Tulu 2 DPO 70B (Q4_K_M, needs ~41.4 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/tulu-2-dpo-70B-GGUF/tree/0f390651b9c7930543c3cca150881d028e115338))
```sh
lms get https://huggingface.co/TheBloke/tulu-2-dpo-70B-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q2_K (~29.3 GB) `lms get https://huggingface.co/TheBloke/tulu-2-dpo-70B-GGUF@Q2_K -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 Tulu 2 DPO 70B (Q4_K_M, needs ~41.4 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/tulu-2-dpo-70B-GGUF/tree/0f390651b9c7930543c3cca150881d028e115338))
```sh
ollama run hf.co/TheBloke/tulu-2-dpo-70B-GGUF:Q4_K_M
```
Other versions: Q2_K (~29.3 GB) `ollama run hf.co/TheBloke/tulu-2-dpo-70B-GGUF:Q2_K`
### Other ways to run it
1. Run it as published (BF16, needs ~137.95 GB, from [huggingface.co/allenai](https://huggingface.co/allenai/tulu-2-dpo-70b/tree/0ab5c875f0070d5aee8d36bc55f41de440a13f02))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='allenai/tulu-2-dpo-70b', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/allenai](https://huggingface.co/allenai/tulu-2-dpo-70b)
[interactive version](https://whatmodel.sh/model/tulu-2-dpo-70b) · [JSON](https://whatmodel.sh/api?format=json&all=1)