[whatmodel.sh](../../README.md) / Tulu 2 7B
# Tulu 2 7B
Allen Institute for AI · released 2023-11 · 7B params · AI2 ImpACT Low-risk; Llama 2 base terms
**score 37.8** ±1.6 · #599 of 732 · from 6 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 Tulu 2 7B among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁●▃▅▇█▇▅▄▃▁ #454 of 469* 19% 1
math ▁▁▃▅●▆▄█▄▂▁▁ #318 of 431* 19% 1
coding · #454 of 469 · 19% of its score
Beats [Molmo 7B-D (0924)](molmo-7b-d.md) here, though they rank higher overall. Trails [Falcon3 1B Instruct](falcon3-1b.md), [Gemma 2B IT](gemma-2b.md), [Granite 3.0 1B-A400M Instruct](granite-3-0-1b-a400m.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Tulu 2: CodexEval pass@10 (maker) 36.9% ▃▃●·▆▃▃█▃▂▂▅ beats 4/25 easy · avg 83% 19% sharp · at its level
math · #318 of 431 · 19% of its score
Beats [GPT-4 (0613)](gpt-4-0613.md), [Athene-70B](athene-70b.md), [Gemma 2 27B IT](gemma-2-27b.md) here, though they rank higher overall. Trails [StarCoder2 7B](starcoder2-7b.md), [DeepSeek R1 Distill Qwen 1.5B](deepseek-r1-distill-qwen-1-5b.md), [Tulu 2 DPO 7B](tulu-2-dpo-7b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K (maker) 34.0% ▆▂▅●▅▅▃▆···▂ beats 10/25 easy · avg 72% 19% sharp · at its level
other tests (4)
test result vs the others in its run difficulty counts why
BIG-Bench Hard (maker) 48.5% ▁·····▂●▂▅·▁ beats 8/25 easy · avg 74% 35% sharp · at its level
MMLU (maker) 50.4% ▁▁▃▃▄●▄▁█··▁ beats 9/25 medium · avg 73% 16% at its level
Open PT-LLM Leaderboard average (… 63.0% ▃▁▁▁▁▁▁▁●█▅▁ beats 100/276 medium · avg 72% 6.0% at its level · noisy
Tulu 2: TyDiQA gold passage, 1-sh… 46.4% ▂··▁▃▂▂●█▅▁▂ beats 10/25 medium · avg 59% 4.1% blunt · at its level
source results share of the score
Tulu 2 (submitted / maker-reported) 5 93% ▇▇▇▇▇▇▇▇▇▇▇
Open Portuguese LLM Leaderboard 1 6% ▇
prior: pull toward the middle 1% ▇
weighted average = 37.8
Licences: Tulu 2 ([CC BY 4.0](../../licenses/CC-BY-4.0.txt)) · 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 7B (Q4_K_M, needs ~4.1 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/tulu-2-7B-GGUF/tree/388feb9dc1b3f9c31913c01050fd783596e6d89e))
```sh
llama cli -hf TheBloke/tulu-2-7B-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-2-7B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.2 GB) `llama cli -hf TheBloke/tulu-2-7B-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 7B (Q4_K_M, needs ~4.1 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/tulu-2-7B-GGUF/tree/388feb9dc1b3f9c31913c01050fd783596e6d89e))
```sh
lms get https://huggingface.co/TheBloke/tulu-2-7B-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-2-7B-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 7B (Q4_K_M, needs ~4.1 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/tulu-2-7B-GGUF/tree/388feb9dc1b3f9c31913c01050fd783596e6d89e))
```sh
ollama run hf.co/TheBloke/tulu-2-7B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.2 GB) `ollama run hf.co/TheBloke/tulu-2-7B-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-2-7b/tree/3c6e328ae91fabdd0daf09de16887de9615c1f66))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='allenai/tulu-2-7b', 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-2-7b)
[interactive version](https://whatmodel.sh/model/tulu-2-7b) · [JSON](https://whatmodel.sh/api?format=json&all=1)