whatmodel.sh / MAP-Neo 7B Instruct v0.1

MAP-Neo 7B Instruct v0.1

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MAP-Neo 7B Instruct v0.1 is an open-weights language model from M-A-P, released May 2024. Its published size is 7.79B parameters. Licence: apache-2.0.

Catalogue identity notes

Maker repo m-a-p/neo_7b_instruct_v0.1 @e31a704: licence tag apache-2.0. 7,793,716,224 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-05: month the maker's Hugging Face repo was created (createdAt 2024-05-28).

38.7±3.1#732 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 MAP-Neo 7B Instruct v0.1, out of 1132 with a score, then more from M-A-P.

#modelscorewhereRAMreleased
894Mistral-7B-codealpaca-lora Nondzu39±1local~6.1GB2023-10
895speechless-code-mistral-7b-v1.0 uukuguy39±1local~17GB2023-10
896MAP-Neo 7B Instruct v0.1 M-A-P39±3local~6.4GB2024-05
897Falcon-H1 3B Instruct TII39±4local~3.4GB2025-05
898StableLM Zephyr 3B Stability AI39±7local~3.2GB2023-11
more from M-A-P
571OpenCodeInterpreter-DS-33B M-A-P50±1local~22GB2024-02
600OpenCodeInterpreter-DS-6.7B M-A-P49±1local~5.8GB2024-02

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)25.0192 WB-Score ✎▁·▁▁·▃█▄▇▆▅▆ beats 7/52avg 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 MAP-Neo 7B Instruct v0.1 · needs ~4.7 GB · from huggingface.co/mradermacher
    chat in the terminal
    llama cli -hf mradermacher/neo_7b_instruct_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/neo_7b_instruct_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 MAP-Neo 7B Instruct v0.1 · needs ~4.7 GB · from huggingface.co/mradermacher

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mradermacher/neo_7b_instruct_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 MAP-Neo 7B Instruct v0.1 · needs ~4.7 GB · from huggingface.co/mradermacher
    chat in the terminal
    ollama run hf.co/mradermacher/neo_7b_instruct_v0.1-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~15.59 GB · from huggingface.co/m-a-p (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='m-a-p/neo_7b_instruct_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/m-a-p ↗

$ 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= 38.7

$ 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