[whatmodel.sh](../../README.md) / RedPajama-INCITE Base 3B v1
# RedPajama-INCITE Base 3B v1
Together · released 2023-05 · 2.8B params · apache-2.0
**score 24.8** ±3.0 · #687 of 732 · from 5 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 RedPajama-INCITE Base 3B v1 among every model on that task or test, worst to best.
other tests (5)
test result vs the others in its run difficulty counts why
WinoGrande (maker) 63.2% ▁▁▁▁▂▁▁▅●█▃▁ beats 50/131 easy · avg 86% 37% small run
HellaSwag (maker) 63.2% ▁▁▁▂▂▂▄●█▇▄▂ beats 50/135 very easy · avg 89% 27% at its level · small run
LAMBADA (OpenAI version), last-wo… 65.4% ▁·····▁▁▃▃●█ beats 39/86 very easy · avg 87% 12% blunt · at its level · small run
Open PT-LLM Leaderboard average (… 29.7% ●▁▁▁▁▁▁▁▂█▅▁ beats 3/276 medium · avg 72% 11% noisy
PIQA (maker) 74.7% ▁▁▁▂▂▃●█▅▃▁▁ beats 43/123 easy · avg 86% 9.4% blunt · at its level · small run
source results share of the score
RedPajama (submitted / maker-reported) 4 85% ▇▇▇▇▇▇▇▇▇▇
Open Portuguese LLM Leaderboard 1 11% ▇
prior: pull toward the middle 4% ▇
weighted average = 24.8
Licences: RedPajama ([CC BY 4.0](../../licenses/redpajama-2024-original-zero-shot-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 RedPajama-INCITE Base 3B v1 (Q4_K_M, needs ~1.8 GB, from [huggingface.co/kikimuncher2](https://huggingface.co/kikimuncher2/RedPajama-INCITE-Base-3B-v1-Q4_K_M-GGUF/tree/875d39f7f31a56cd37ad5f89261c886c27944314))
```sh
llama cli -hf kikimuncher2/RedPajama-INCITE-Base-3B-v1-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 kikimuncher2/RedPajama-INCITE-Base-3B-v1-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 RedPajama-INCITE Base 3B v1 (Q4_K_M, needs ~1.8 GB, from [huggingface.co/kikimuncher2](https://huggingface.co/kikimuncher2/RedPajama-INCITE-Base-3B-v1-Q4_K_M-GGUF/tree/875d39f7f31a56cd37ad5f89261c886c27944314))
```sh
lms get https://huggingface.co/kikimuncher2/RedPajama-INCITE-Base-3B-v1-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 RedPajama-INCITE Base 3B v1 (Q4_K_M, needs ~1.8 GB, from [huggingface.co/kikimuncher2](https://huggingface.co/kikimuncher2/RedPajama-INCITE-Base-3B-v1-Q4_K_M-GGUF/tree/875d39f7f31a56cd37ad5f89261c886c27944314))
```sh
ollama run hf.co/kikimuncher2/RedPajama-INCITE-Base-3B-v1-Q4_K_M-GGUF:q4_k_m
```
### Other ways to run it
1. Run it as published (fp16, needs ~5.69 GB, from [huggingface.co/togethercomputer](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-3B-v1/tree/094fbdd0c911feb485ce55de1952ab2e75277e1e))
```sh
uv run --with torch --with 'transformers<5' python -c "from transformers import pipeline; print(pipeline('text-generation', model='togethercomputer/RedPajama-INCITE-Base-3B-v1')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/togethercomputer](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-3B-v1)
[interactive version](https://whatmodel.sh/model/redpajama-incite-base-3b-v1) · [JSON](https://whatmodel.sh/api?format=json&all=1)