[whatmodel.sh](../../README.md) / Llama-3-Instruct-8B-SimPO-v0.2
# Llama-3-Instruct-8B-SimPO-v0.2
Princeton NLP · released 2024-07 · 8.03B params
**score 40.3** ±1.1 · #561 of 732 · from 4 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 Llama-3-Instruct-8B-SimPO-v0.2 among every model on that task or test, worst to best.
task vs the others rank counts tests
math ▁▁●▅█▆▄█▄▂▁▁ #404 of 431* 29% 1
math · #404 of 431 · 29% of its score
Beats [Command R (03-2024)](command-r-03-2024.md), [Mistral 7B Instruct v0.3](mistral-7b-instruct-v0-3.md) here, though they rank higher overall. Trails [GPT-Neo 125M](gpt-neo-125m.md), [OPT-125M](opt-125m.md), [GPT-2 XL (1.5B)](gpt-2-xl.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K (ZeroEval, zero-shot) 57.5% ▁●·▁▄█▅▅▅███ beats 3/43 very easy · avg 87% 29% sharp · at its level
other tests (3)
test result vs the others in its run difficulty counts why
MMLU-Redux (ZeroEval, 2,778 quest… 55.2% ▁·▁●▄▄▄▅█▄▂▁ beats 2/53 easy · avg 75% 56% at its level
WildBench v2 WB-Score (task macro) 37.1554 WB-Score ▁·▁▁·▃█▄●▆▅▆ beats 24/52 avg 43.019 WB-Score 12% sharp
Open PT-LLM Leaderboard average (… 68.4% ▃▁▁▁▁▁▁▁▂●▅▁ beats 153/276 medium · avg 72% 2.6% at its level · noisy
source results share of the score
WildEval ZeroEval published results 2 84% ▇▇▇▇▇▇▇▇▇▇
WildBench v2 leaderboard data 1 12% ▇
Open Portuguese LLM Leaderboard 1 3% ▇
prior: pull toward the middle 0% ▇
weighted average = 40.3
Licences: WildEval ZeroEval published results ([Apache-2.0](../../licenses/zeroeval-published-results-Apache-2.0.txt)) · WildBench v2 leaderboard data ([Apache-2.0](https://github.com/allenai/WildBench/blob/d6b8dcaf377d173d031980f97c16e1a82618c03d/LICENSE)) · 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 Llama-3-Instruct-8B-SimPO-v0.2 (Q4_K_M, needs ~4.9 GB, from [huggingface.co/matrixportalx](https://huggingface.co/matrixportalx/Llama-3-Instruct-8B-SimPO-v0.2-Q4_K_M-GGUF/tree/d4e42980129edee7bacdacc8afe5ed7f449964d2))
```sh
llama cli -hf matrixportalx/Llama-3-Instruct-8B-SimPO-v0.2-Q4_K_M-GGUF:q4_k_m
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf matrixportalx/Llama-3-Instruct-8B-SimPO-v0.2-Q4_K_M-GGUF:q4_k_m
```
### 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 Llama-3-Instruct-8B-SimPO-v0.2 (Q4_K_M, needs ~4.9 GB, from [huggingface.co/matrixportalx](https://huggingface.co/matrixportalx/Llama-3-Instruct-8B-SimPO-v0.2-Q4_K_M-GGUF/tree/d4e42980129edee7bacdacc8afe5ed7f449964d2))
```sh
lms get https://huggingface.co/matrixportalx/Llama-3-Instruct-8B-SimPO-v0.2-Q4_K_M-GGUF@q4_k_m -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run Llama-3-Instruct-8B-SimPO-v0.2 (Q4_K_M, needs ~4.9 GB, from [huggingface.co/matrixportalx](https://huggingface.co/matrixportalx/Llama-3-Instruct-8B-SimPO-v0.2-Q4_K_M-GGUF/tree/d4e42980129edee7bacdacc8afe5ed7f449964d2))
```sh
ollama run hf.co/matrixportalx/Llama-3-Instruct-8B-SimPO-v0.2-Q4_K_M-GGUF:q4_k_m
```
### Other ways to run it
1. Run it as published (BF16, needs ~16.06 GB, from [huggingface.co/princeton-nlp](https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-SimPO-v0.2/tree/2b18bdce3e3e8f588f48ff62350db9620ceefde3))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='princeton-nlp/Llama-3-Instruct-8B-SimPO-v0.2', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/princeton-nlp](https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-SimPO-v0.2)
[interactive version](https://whatmodel.sh/model/llama-3-instruct-8b-simpo-v0-2) · [JSON](https://whatmodel.sh/api?format=json&all=1)