whatmodel.sh / Llama-3-8B-Magpie-Align-v0.1

Llama-3-8B-Magpie-Align-v0.1

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Llama-3-8B-Magpie-Align-v0.1 is an open-weights language model from Magpie-Align, released June 2024. Its published size is 8.03B parameters. Licence: llama3.

Catalogue identity notes

Maker repo Magpie-Align/Llama-3-8B-Magpie-Align-v0.1 @f5f3f01: licence tag llama3. 8,030,261,248 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-06: month the maker's Hugging Face repo was created (createdAt 2024-06-29).

46.7±3.1#531 of 949 LLMsfrom 1 benchmark result across 1 source* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of Llama-3-8B-Magpie-Align-v0.1, out of 1132 with a score.

#modelscorewhereRAMreleased
671Gemma 4 E2B Google47±1api $0.050/M +local~5.1GB2026-04
672Magicoder-S-CL-7B UIUC (ise-uiuc)47±1local~5.8GB2023-12
673Llama-3-8B-Magpie-Align-v0.1 Magpie-Align47±3local~6.6GB2024-06
674Llama-3-8B-Instruct-Gradient-1048k Gradient AI47±5local~6.2GB2024-04
675Aya Expanse 8B Cohere47±2local~6.2GB2024-10

see all LLMs · find the ones that fit your machine

$ what it's good at

other tests1 not in a task · 96% of its score
testresultvs the others in its rundifficultycountswhy
WildBench v2 WB-Score (task macro)39.2902 WB-Score ✎▁·▁▁·▃█▄▇▆▅▆ beats 29/52avg 45.199 WB-Score▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 96%sharp

$ where to run it

llama.cpp · recommended

version

  1. Install llama.cpp · Homebrew and winget installs name the commands llama-cli and llama-server instead.
    macOS / Linux
    curl -LsSf https://llama.app/install.sh | sh
    Windows
    irm https://llama.app/install.ps1 | iex

    other ways to install ↗

  2. Run Llama-3-8B-Magpie-Align-v0.1 · needs ~4.9 GB · from huggingface.co/mradermacher
    chat in the terminal
    llama cli -hf mradermacher/Llama-3-8B-Magpie-Align-v0.1-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf mradermacher/Llama-3-8B-Magpie-Align-v0.1-GGUF:Q4_K_M

LM Studio

version

  1. Install LM Studio · Open the app once before using the lms command.

    download it (macOS / Windows / Linux) ↗

  2. Run Llama-3-8B-Magpie-Align-v0.1 · needs ~4.9 GB · from huggingface.co/mradermacher

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mradermacher/Llama-3-8B-Magpie-Align-v0.1-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

version

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run Llama-3-8B-Magpie-Align-v0.1 · needs ~4.9 GB · from huggingface.co/mradermacher
    chat in the terminal
    ollama run hf.co/mradermacher/Llama-3-8B-Magpie-Align-v0.1-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~16.06 GB · from huggingface.co/Magpie-Align (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='Magpie-Align/Llama-3-8B-Magpie-Align-v0.1', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/Magpie-Align ↗

$ how the score was worked out · WildBench v2 leaderboard data… 96%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
WildBench v2 leaderboard data (Ai2)1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 96%Apache-2.0
prior · pull toward the middle▇ 4%
weighted average= 46.7

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json