[whatmodel.sh](../../README.md) / Llama 3 8B (base)
# Llama 3 8B (base)
Meta · released 2024-04 · 8.03B params · llama3
**score 37.3** ±0.8 · #605 of 732 · from 9 benchmark results across 5 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 8B (base) among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁●▅▇█▇▅▄▃▁ #426 of 469 84% 3
coding · #426 of 469 · 84% 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), [Granite 3B Code Base (2K)](granite-3b-code-base-2k.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 33.5% ▁·▁▁▃●▆▅█▃▁▁ beats 18/99 easy · avg 76% 44% sharp · at its level
HumanEval+ 29.3% ▁·▁▁▁▃●▄▄█▃▁ beats 18/99 easy · avg 70% 33% sharp · at its level
MBPP+ 51.6% ▁···▁▁▂●▇█▆▂ beats 21/75 easy · avg 66% 7.0% at its level
other tests (6)
test result vs the others in its run difficulty counts why
BigCodeBench (Complete) 28.8% ▂▁▁●▂▅▇█▆▂▂▂ beats 9/58 medium · avg 48% 5.6% at its level
MedQA (USMLE, 4 options) 59.7% ▁▁··▄▁▁··▄▃● beats 16/17 medium · avg 71% 4.3% sharp · at its level
Open Persian LLM Leaderboard v2 a… 47.6% ▂▄▂▃●▄▃▆▃▅▂▂ beats 23/81 medium · avg 57% 2.7% blunt · at its level
MedMCQA 57.5% ▂▂··▄▄▂▂·▄█● beats 16/17 medium · avg 63% 2.0% at its level
EuroEval Life in the UK (English … 82.3% ▁▁▁▁▁▁▁▂▄●▄▃ beats 77/146 easy · avg 82% 1.0% at its level · noisy
PubMedQA 74.8% ▂·▂▆·▄▂▄▄▂●▂ beats 14/17 medium · avg 75% 0.3% blunt · at its level
source results share of the score
EvalPlus / EvalPerf published evaluatio… 3 84% ▇▇▇▇▇▇▇▇▇▇
Open Medical-LLM Leaderboard results 3 7% ▇
BigCodeBench paper 1 6% ▇
Open Persian LLM Leaderboard v2 1 3% ▇
EuroEval leaderboards 1 1% ▇
prior: pull toward the middle 0% ▇
weighted average = 37.3
Licences: EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-Apache-2.0.txt)) · Open Medical-LLM Leaderboard results ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · BigCodeBench paper ([CC BY 4.0](http://creativecommons.org/licenses/by/4.0/)) · Open Persian LLM Leaderboard v2 ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE))
## 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 8B (base) (Q4_K_M, needs ~4.9 GB, from [huggingface.co/QuantFactory](https://huggingface.co/QuantFactory/Meta-Llama-3-8B-GGUF/tree/1ca85c857dce892b673b988ad0aa83f2cb1bbd19))
```sh
llama cli -hf QuantFactory/Meta-Llama-3-8B-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf QuantFactory/Meta-Llama-3-8B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~8.5 GB) `llama cli -hf QuantFactory/Meta-Llama-3-8B-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 Llama 3 8B (base) (Q4_K_M, needs ~4.9 GB, from [huggingface.co/QuantFactory](https://huggingface.co/QuantFactory/Meta-Llama-3-8B-GGUF/tree/1ca85c857dce892b673b988ad0aa83f2cb1bbd19))
```sh
lms get https://huggingface.co/QuantFactory/Meta-Llama-3-8B-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 (~8.5 GB) `lms get https://huggingface.co/QuantFactory/Meta-Llama-3-8B-GGUF@Q8_0 -y` · 4bit (~5.3 GB, Mac only) `lms get https://huggingface.co/mlx-community/Meta-Llama-3-8B-4bit -y` · 8bit (~9 GB, Mac only) `lms get https://huggingface.co/mlx-community/Meta-Llama-3-8B-8bit -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 Llama 3 8B (base) (Q4_K_M, needs ~4.9 GB, from [huggingface.co/QuantFactory](https://huggingface.co/QuantFactory/Meta-Llama-3-8B-GGUF/tree/1ca85c857dce892b673b988ad0aa83f2cb1bbd19))
```sh
ollama run hf.co/QuantFactory/Meta-Llama-3-8B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~8.5 GB) `ollama run hf.co/QuantFactory/Meta-Llama-3-8B-GGUF:Q8_0`
### MLX (Mac)
1. Install MLX (Mac). Apple silicon Macs only, text only.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run Llama 3 8B (base) (4bit, needs ~5.3 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Meta-Llama-3-8B-4bit/tree/7f296f1dd0e720f7daa69ed648ed7b193373ca11))
```sh
mlx_lm.chat --model mlx-community/Meta-Llama-3-8B-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Meta-Llama-3-8B-4bit
```
Other versions: 8bit (~9 GB, Mac only) `mlx_lm.chat --model mlx-community/Meta-Llama-3-8B-8bit`
### Other ways to run it
1. Run it as published (BF16, needs ~16.06 GB, from [huggingface.co/meta-llama](https://huggingface.co/meta-llama/Meta-Llama-3-8B/tree/8cde5ca8380496c9a6cc7ef3a8b46a0372a1d920))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='meta-llama/Meta-Llama-3-8B', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/meta-llama](https://huggingface.co/meta-llama/Meta-Llama-3-8B)
[interactive version](https://whatmodel.sh/model/llama-3-8b-base) · [JSON](https://whatmodel.sh/api?format=json&all=1)