whatmodel.sh / MegaBeam-Mistral-7B-512k

MegaBeam-Mistral-7B-512k

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MegaBeam-Mistral-7B-512k is an open-weights language model from AWS (aws-prototyping), released July 2024. Its published size is 7.24B parameters. Licence: apache-2.0.

Catalogue identity notes

Maker repo aws-prototyping/MegaBeam-Mistral-7B-512k @7a9bfb1: licence tag apache-2.0. 7,241,732,096 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-07: month the maker's Hugging Face repo was created (createdAt 2024-07-30).

48.8±4.4#367 of 719 LLMsfrom 3 benchmark results across 2 sources

$ how it compares

The LLMs ranked either side of MegaBeam-Mistral-7B-512k, out of 828 with a score.

#modelscorewhereRAMreleased
436Pixtral 12B (2409) Mistral49±2local~9.6GB2024-09
437Nova Lite Amazon49±2api $0.10/M2024-12
438MegaBeam-Mistral-7B-512k AWS (aws-prototyping)49±4local~6.1GB2024-07
439GPT-4o Audio Preview (2024-12-17) OpenAI49±2comparison only2024-12
440Claude 3 Sonnet Anthropic49±1comparison only2024-02

see all LLMs · find the ones that fit your machine

$ what it's good at

other tests3 not in a task · 91% of its score
testresultvs the others in its rundifficultycountswhy
RULER 64k83.7% ✎▁▁·▁·▂▃▄█▂·▁ beats 11/22easy · avg 83%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 39%typical
Open PT-LLM Leaderboard average (9 Portuguese tasks)62.6% ✎▃▁▁▁▁▁▁▁▃█▅▁ beats 96/276medium · avg 73%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 38%at its level · noisy
RULER 128k83.7% ✎▄··▂▄▂▆██▄▄▂ beats 20/21easy · avg 68%▇▇▇▇▇▇ 15%at its level · noisy

$ 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 MegaBeam-Mistral-7B-512k · needs ~4.4 GB · from huggingface.co/bartowski
    chat in the terminal
    llama cli -hf bartowski/MegaBeam-Mistral-7B-512k-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf bartowski/MegaBeam-Mistral-7B-512k-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 MegaBeam-Mistral-7B-512k · needs ~4.4 GB · from huggingface.co/bartowski

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/bartowski/MegaBeam-Mistral-7B-512k-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 MegaBeam-Mistral-7B-512k · needs ~4.4 GB · from huggingface.co/bartowski
    chat in the terminal
    ollama run hf.co/bartowski/MegaBeam-Mistral-7B-512k-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~14.48 GB · from huggingface.co/aws-prototyping
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='aws-prototyping/MegaBeam-Mistral-7B-512k', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Weights and documentation: huggingface.co/aws-prototyping ↗

$ how the score was worked out · NVIDIA RULER repository READM… 53%, Open Portuguese LLM Leaderboa… 38%

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
NVIDIA RULER repository README results table2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 53%Apache-2.0
Open Portuguese LLM Leaderboard (archived)1▇▇▇▇▇▇▇▇▇▇▇ 38%Apache-2.0
prior · pull toward the middle▇▇▇ 9%
weighted average= 48.8

$ 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