whatmodel.sh / Yi-1.5 9B Chat 16K

Yi-1.5 9B Chat 16K

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Yi-1.5 9B Chat 16K is an open-weights language model from 01.AI, released May 2024. Its published size is 8.83B parameters and a 16k-token context window. Licence: apache-2.0.

Catalogue identity notes

Maker repo 01-ai/Yi-1.5-9B-Chat-16K @2f8753b: licence tag apache-2.0. 8,829,407,232 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-05: month the maker's Hugging Face repo was created (createdAt 2024-05-15).

46.6±3.9#537 of 949 LLMsfrom 2 benchmark results across 2 sources* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of Yi-1.5 9B Chat 16K, out of 1132 with a score, then more from 01.AI.

#modelscorewhereRAMreleased
678InternLM2.5 7B Chat 1M Shanghai AI Laboratory (InternLM)47±4local~6.4GB2024-06
679PLLuM-8x7B-nc-chat (2412) PLLuM consortium (CYFRAGOVPL)47±4local~31GB2025-02
680Yi-1.5 9B Chat 16K 01.AI47±4local~7.1GB2024-05
681Mistral7B-PairRM-SPPO UCLA AGI (SPPO)47±2local~6.1GB2024-05
682EuroLLM 9B Instruct UTTER (EuroLLM)47±3local~6.9GB2024-11
more from 01.AI
404Yi-Lightning 01.AI55±1api price unknown2024-10
506Yi-Large Preview 01.AI52±1comparison only2024-05
547Yi-Large 01.AI51±1comparison only2024-05

see all LLMs · find the ones that fit your machine

$ what it's good at

other tests2 not in a task · 93% of its score
testresultvs the others in its rundifficultycountswhy
BRIDGE clinical text, zero-shot average (87 tasks)28.8% ✎▁▁▁▁▁▂▂▃▄▆█▃ beats 26/97hard · avg 32%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 63%blunt · at its level
Open PT-LLM Leaderboard average (9 Portuguese tasks)67.8% ✎▃▁▁▁▁▁▁▁▂█▅▁ beats 140/276medium · avg 73%▇▇▇▇▇▇▇▇▇▇▇▇ 30%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 Yi-1.5 9B Chat 16K · needs ~5.3 GB · from huggingface.co/mradermacher
    chat in the terminal
    llama cli -hf mradermacher/Yi-1.5-9B-Chat-16K-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf mradermacher/Yi-1.5-9B-Chat-16K-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 Yi-1.5 9B Chat 16K · needs ~5.3 GB · from huggingface.co/mradermacher

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mradermacher/Yi-1.5-9B-Chat-16K-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 Yi-1.5 9B Chat 16K · needs ~5.3 GB · from huggingface.co/mradermacher
    chat in the terminal
    ollama run hf.co/mradermacher/Yi-1.5-9B-Chat-16K-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~17.66 GB · from huggingface.co/01-ai (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='01-ai/Yi-1.5-9B-Chat-16K', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/01-ai ↗

$ how the score was worked out · BRIDGE Medical Leaderboard (H… 63%, Open Portuguese LLM Leaderboa… 30%

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
BRIDGE Medical Leaderboard (Harvard Medical School / Brigham and Women's YLab)1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 63%MIT
Open Portuguese LLM Leaderboard (archived)1▇▇▇▇▇▇▇▇▇ 30%Apache-2.0
prior · pull toward the middle▇▇ 7%
weighted average= 46.6

$ 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