[whatmodel.sh](../../README.md) / Tulu 2 70B
# Tulu 2 70B
Allen Institute for AI · released 2023-11 · 70B params · AI2 ImpACT Low-risk; Llama 2 base terms
**score 47.3** ±1.7 · #422 of 732 · from 5 benchmark results across 1 source · † no independently run results verified
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Tulu 2 70B among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃●▇█▇▅▄▃▁ #389 of 469* 14% 1
math ▁▁▃▅█●▄█▄▂▁▁ #212 of 431* 21% 1
coding · #389 of 469 · 14% of its score
Beats [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md), [DeepSeek R1 Distill Qwen 7B](deepseek-r1-distill-qwen-7b.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) 68.5% ▃▃▃·▆▃▃●▃▂▂▅ beats 14/25 easy · avg 83% 14% sharp
math · #212 of 431 · 21% 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), [Olmo 3 7B Instruct](olmo-3-7b-instruct.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K (maker) 73.0% ▆▂▅█▅▅▃●···▂ beats 22/25 easy · avg 72% 21% sharp · at its level
other tests (3)
test result vs the others in its run difficulty counts why
BIG-Bench Hard (maker) 68.4% ▁·····▂█▂●·▁ beats 23/25 easy · avg 74% 42% sharp · at its level
MMLU (maker) 67.3% ▁▁▃▃▄█▄▁●··▁ beats 22/25 medium · avg 73% 18% at its level
Tulu 2: TyDiQA gold passage, 1-sh… 53.6% ▂··▁▃▂▂▂●▅▁▂ beats 18/25 medium · avg 59% 4.3% blunt · at its level
source results share of the score
Tulu 2 (submitted / maker-reported) 5 99% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle 1% ▇
weighted average = 47.3
Licences: Tulu 2 ([CC BY 4.0](../../licenses/CC-BY-4.0.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 70B (Q4_K_M, needs ~41.4 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/tulu-2-70b-GGUF/tree/0cdc0e86f122f879a9e90439e0711158d18ea7c4))
```sh
llama cli -hf mradermacher/tulu-2-70b-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf mradermacher/tulu-2-70b-GGUF:Q4_K_M
```
Other versions: Q2_K (~25.5 GB) `llama cli -hf mradermacher/tulu-2-70b-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 70B (Q4_K_M, needs ~41.4 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/tulu-2-70b-GGUF/tree/0cdc0e86f122f879a9e90439e0711158d18ea7c4))
```sh
lms get https://huggingface.co/mradermacher/tulu-2-70b-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 (~25.5 GB) `lms get https://huggingface.co/mradermacher/tulu-2-70b-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 70B (Q4_K_M, needs ~41.4 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/tulu-2-70b-GGUF/tree/0cdc0e86f122f879a9e90439e0711158d18ea7c4))
```sh
ollama run hf.co/mradermacher/tulu-2-70b-GGUF:Q4_K_M
```
Other versions: Q2_K (~25.5 GB) `ollama run hf.co/mradermacher/tulu-2-70b-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-2-70b/tree/be647298930cfc399d984fb36b84261de8bd9ce6))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='allenai/tulu-2-70b', 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-2-70b)
[interactive version](https://whatmodel.sh/model/tulu-2-70b) · [JSON](https://whatmodel.sh/api?format=json&all=1)