whatmodel.sh / Mistral-7B-codealpaca-lora

Mistral-7B-codealpaca-lora

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Mistral-7B-codealpaca-lora is an open-weights language model from Nondzu, released October 2023. Its published size is 7.24B parameters. Licence: apache-2.0.

Catalogue identity notes

Maker repo Nondzu/Mistral-7B-codealpaca-lora @8f97cd1: licence tag apache-2.0. 7,241,732,096 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2023-10: month the maker's Hugging Face repo was created (createdAt 2023-10-25).

38.8±0.8#730 of 949 LLMsfrom 2 benchmark results across 1 source* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of Mistral-7B-codealpaca-lora, out of 1132 with a score.

#modelscorewhereRAMreleased
892Granite 8B Code Base (4K) IBM39±1local~6.6GB2024-04
893StarCoder2 15B BigCode39±1local~11GB2024-02
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

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▆██▇▆▄▃▁ #392 of 469 * · 100% of its score · 2 tests

Beats DeepSeek R1 Distill Qwen 7B, Qwen3.5 2B, DeepSeek R1 Distill Llama 8B here, though they rank higher overall. Trails Qwen2.5 1.5B Instruct, WizardLM 13B V1.2, Falcon3 3B Instruct here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
HumanEval48.2% ✎▁·▁▁▃▄▆▅█▃▁▁ beats 34/99easy · avg 79%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 58%sharp · at its level
HumanEval+42.1% ✎▁·▁▁▁▃▄▄▄█▃▁ beats 34/99easy · avg 74%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 41%sharp · at its level

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

    open in LM Studio ↗

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

Other ways to run it

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

Official weights and documentation: huggingface.co/Nondzu ↗

$ how the score was worked out · EvalPlus / EvalPerf published… 100%

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
EvalPlus / EvalPerf published evaluation runs2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 100%Apache-2.0
prior · pull toward the middle▇ 0%
weighted average= 38.8

$ 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