[whatmodel.sh](../../README.md) / Tulu 2 DPO 7B
# Tulu 2 DPO 7B
Allen Institute for AI · released 2023-11 · 7B params · AI2 ImpACT Low-risk; Llama 2 base terms
**score 37.1** ±1.3 · #608 of 732 · from 8 benchmark results across 3 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 7B among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁●▃▅▇█▇▅▄▃▁ #450 of 469* 13% 1
chat & writing ▁▁▁●▆▄▆█▆▇▇▃ #370 of 402* 36% 2
math ▁▁▃▅●▆▄█▄▂▁▁ #314 of 431* 12% 1
coding · #450 of 469 · 13% of its score
Beats [Molmo 7B-D (0924)](molmo-7b-d.md), [Tulu 2 7B](tulu-2-7b.md) here, though they rank higher overall. Trails [Falcon3 1B Instruct](falcon3-1b.md), [Granite 3.0 1B-A400M Instruct](granite-3-0-1b-a400m.md), [Gemma 1.1 2B IT](gemma-1-1-2b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Tulu 2: CodexEval pass@10 (maker) 40.0% ▃▃●·▆▃▃█▃▂▂▅ beats 5/25 easy · avg 83% 13% sharp · at its level
chat & writing · #370 of 402 · 36% of its score
Beats [Code Llama 70B Instruct](codellama-70b-instruct.md), [Gemma 4 E2B](gemma-4-e2b.md), [Phi-3-mini-128k-instruct (Apr 2024)](phi-3-mini-128k-instruct.md) here, though they rank higher overall. Trails [Gemma 3 270M IT](gemma-3-270m.md), [SmolLM2 360M Instruct](smollm2-360m.md), [Granite 4.0 350M](granite-4-0-350m.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AlpacaEval 2 length-controlled 9.2% ▁▂▂●▆▆█▆▄▄▁▁ beats 20/122 medium · avg 35% 32% sharp
AlpacaEval 1.0 win rate (vs text-… 84.2% ▂▂·▆·▇▃●█▆▄▂ beats 22/45 very easy · avg 94% 4.0% sharp · at its level · noisy
math · #314 of 431 · 12% of its score
Beats [Molmo 2 8B](molmo-2-8b.md), [GPT-4 (0613)](gpt-4-0613.md), [Athene-70B](athene-70b.md) here, though they rank higher overall. Trails [DeepSeek R1 Distill Qwen 1.5B](deepseek-r1-distill-qwen-1-5b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K (maker) 34.5% ▆▂▅●▅▅▃▆···▂ beats 11/25 easy · avg 72% 12% sharp · at its level
other tests (4)
test result vs the others in its run difficulty counts why
BIG-Bench Hard (maker) 45.5% ▁·····▂●▂▅·▁ beats 6/25 easy · avg 74% 22% sharp · at its level
MMLU (maker) 50.7% ▁▁▃▃▄●▄▁█··▁ beats 10/25 medium · avg 73% 10% at its level
Open PT-LLM Leaderboard average (… 63.9% ▃▁▁▁▁▁▁▁▂●▅▁ beats 106/276 medium · avg 72% 3.9% at its level · noisy
Tulu 2: TyDiQA gold passage, 1-sh… 44.5% ▂··▁▃▂●▂█▅▁▂ beats 9/25 medium · avg 59% 2.7% blunt · at its level
source results share of the score
Tulu 2 (submitted / maker-reported) 5 59% ▇▇▇▇▇▇▇
AlpacaEval 1.0 and 2.0 leaderboards 2 36% ▇▇▇▇
Open Portuguese LLM Leaderboard 1 4% ▇
prior: pull toward the middle 1% ▇
weighted average = 37.1
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)) · 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 7B (Q4_K_M, needs ~4.1 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/tulu-2-dpo-7B-GGUF/tree/2a14132f82450fd77b38135f182ec68c8423aa3c))
```sh
llama cli -hf TheBloke/tulu-2-dpo-7B-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-7B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.2 GB) `llama cli -hf TheBloke/tulu-2-dpo-7B-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 Tulu 2 DPO 7B (Q4_K_M, needs ~4.1 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/tulu-2-dpo-7B-GGUF/tree/2a14132f82450fd77b38135f182ec68c8423aa3c))
```sh
lms get https://huggingface.co/TheBloke/tulu-2-dpo-7B-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 (~7.2 GB) `lms get https://huggingface.co/TheBloke/tulu-2-dpo-7B-GGUF@Q8_0 -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 7B (Q4_K_M, needs ~4.1 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/tulu-2-dpo-7B-GGUF/tree/2a14132f82450fd77b38135f182ec68c8423aa3c))
```sh
ollama run hf.co/TheBloke/tulu-2-dpo-7B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.2 GB) `ollama run hf.co/TheBloke/tulu-2-dpo-7B-GGUF:Q8_0`
### Other ways to run it
1. Run it as published (BF16, needs ~13.48 GB, from [huggingface.co/allenai](https://huggingface.co/allenai/tulu-2-dpo-7b/tree/b57ef95260b6d4e726adf64518af038e5673f126))
```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-7b', 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-7b)
[interactive version](https://whatmodel.sh/model/tulu-2-dpo-7b) · [JSON](https://whatmodel.sh/api?format=json&all=1)