[whatmodel.sh](../../README.md) / Tulu 2 DPO 13B
# Tulu 2 DPO 13B
Allen Institute for AI · released 2023-11 · 13B params · AI2 ImpACT Low-risk; Llama 2 base terms
**score 39.4** ±1.3 · #573 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 13B among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁●▅▇█▇▅▄▃▁ #438 of 469* 11% 1
chat & writing ▁▁▁▃●▄▆█▆▇▇▃ #352 of 402* 38% 2
math ▁▁▃▅●▆▄█▄▂▁▁ #279 of 431* 12% 1
coding · #438 of 469 · 11% of its score
Beats [DeepSeek R1 Distill Qwen 7B](deepseek-r1-distill-qwen-7b.md), [Qwen3.5 2B](qwen3-5-2b.md), [DeepSeek R1 Distill Llama 8B](deepseek-r1-distill-llama-8b.md) here, though they rank higher overall. Trails [Llama 3.2 1B Instruct](llama-3-2-1b.md), [SmolLM2 1.7B Instruct](smollm2-1-7b.md), [Mistral 7B Instruct v0.1](mistral-7b-instruct-v0-1.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Tulu 2: CodexEval pass@10 (maker) 48.9% ▃▃▃·●▃▃█▃▂▂▅ beats 6/25 easy · avg 83% 11% sharp · at its level
chat & writing · #352 of 402 · 38% of its score
Beats [Code Llama 70B Instruct](codellama-70b-instruct.md), [Gemma 4 E2B](gemma-4-e2b.md), [QwQ-32B-Preview](qwq-32b-preview.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 H 350M](granite-4-0-h-350m.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AlpacaEval 2 length-controlled 11.6% ▁▂▂▃●▆█▆▄▄▁▁ beats 28/122 medium · avg 35% 35% sharp
AlpacaEval 1.0 win rate (vs text-… 88.1% ▂▂·▆·▇▃▆●▆▄▂ beats 26/45 very easy · avg 94% 3.1% sharp · noisy
math · #279 of 431 · 12% of its score
Beats [Molmo 2 8B](molmo-2-8b.md), [Llama 3.3 70B Instruct](llama-3-3-70b.md), [Mistral Large 2 (2407)](mistral-large-2407.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) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K (maker) 49.5% ▆▂▅█▅●▃▆···▂ beats 16/25 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) 49.4% ▁·····▂●▂▅·▁ beats 12/25 easy · avg 74% 22% sharp · at its level
MMLU (maker) 55.3% ▁▁▃▃▄█●▁█··▁ beats 15/25 medium · avg 73% 10% at its level
Open PT-LLM Leaderboard average (… 65.8% ▃▁▁▁▁▁▁▁▂●▅▁ beats 120/276 medium · avg 72% 3.7% at its level · noisy
Tulu 2: TyDiQA gold passage, 1-sh… 39.7% ▂··▁▃●▂▂█▅▁▂ beats 6/25 medium · avg 59% 2.5% blunt · at its level
source results share of the score
Tulu 2 (submitted / maker-reported) 5 57% ▇▇▇▇▇▇▇
AlpacaEval 1.0 and 2.0 leaderboards 2 38% ▇▇▇▇▇
Open Portuguese LLM Leaderboard 1 4% ▇
prior: pull toward the middle 1% ▇
weighted average = 39.4
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 13B (Q4_K_M, needs ~7.9 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/tulu-2-dpo-13B-GGUF/tree/a90ff0998d096f0e669d8312462687557b36ab0d))
```sh
llama cli -hf TheBloke/tulu-2-dpo-13B-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-13B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~13.8 GB) `llama cli -hf TheBloke/tulu-2-dpo-13B-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 13B (Q4_K_M, needs ~7.9 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/tulu-2-dpo-13B-GGUF/tree/a90ff0998d096f0e669d8312462687557b36ab0d))
```sh
lms get https://huggingface.co/TheBloke/tulu-2-dpo-13B-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 (~13.8 GB) `lms get https://huggingface.co/TheBloke/tulu-2-dpo-13B-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 13B (Q4_K_M, needs ~7.9 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/tulu-2-dpo-13B-GGUF/tree/a90ff0998d096f0e669d8312462687557b36ab0d))
```sh
ollama run hf.co/TheBloke/tulu-2-dpo-13B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~13.8 GB) `ollama run hf.co/TheBloke/tulu-2-dpo-13B-GGUF:Q8_0`
### Other ways to run it
1. Run it as published (BF16, needs ~26.03 GB, from [huggingface.co/allenai](https://huggingface.co/allenai/tulu-2-dpo-13b/tree/8eaeb5c6d70204bfc4dbd14a439c5f0dc12cca43))
```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-13b', 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-13b)
[interactive version](https://whatmodel.sh/model/tulu-2-dpo-13b) · [JSON](https://whatmodel.sh/api?format=json&all=1)