whatmodel.sh / Yi 6B Chat

Yi 6B Chat

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Yi 6B Chat is an open-weights language model from 01.AI, released November 2023. Its published size is 6.06B parameters and a 4k-token context window. Licence: apache-2.0.

Catalogue identity notes

Maker repo 01-ai/Yi-6B-Chat @2dbf63b: licence tag apache-2.0. 6,061,035,520 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2023-11: month the maker's Hugging Face repo was created (createdAt 2023-11-22).

30.2±2.4#857 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 6B Chat, out of 1132 with a score, then more from 01.AI.

#modelscorewhereRAMreleased
1026LFM2 350M Liquid AI30±2local~1.7GB2025-07
1027SmolLM2 1.7B Instruct Hugging Face30±1local~2.6GB2024-10
1028Yi 6B Chat 01.AI30±2local~5.1GB2023-11
1029H2O-Danube2 1.8B Chat H2O.ai30±4local~2.7GB2024-04
1030Llama 3.2 1B Instruct Meta30±1api $0.020/M +local~2.2GB2024-09
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 · 97% of its score
testresultvs the others in its rundifficultycountswhy
LiveBench 2024-07-269.6% ✎▁▁▃▂▂▃▂█▆▃▅▃ beats 3/61medium · avg 41%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 89%typical
Open PT-LLM Leaderboard average (9 Portuguese tasks)60.1% ✎▃▁▁▁▁▁▁▁▃█▅▁ beats 90/276medium · avg 73%▇▇▇ 8.1%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 6B Chat · needs ~3.7 GB · from huggingface.co/mradermacher
    chat in the terminal
    llama cli -hf mradermacher/Yi-6B-Chat-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf mradermacher/Yi-6B-Chat-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 6B Chat · needs ~3.7 GB · from huggingface.co/mradermacher

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mradermacher/Yi-6B-Chat-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 6B Chat · needs ~3.7 GB · from huggingface.co/mradermacher
    chat in the terminal
    ollama run hf.co/mradermacher/Yi-6B-Chat-GGUF:Q4_K_M

MLX (Mac)

version

  1. Install MLX (Mac) · Apple silicon Macs only, text only.
    with uv
    uv tool install mlx-lm
    or
    pip install mlx-lm
  2. Run Yi 6B Chat · needs ~3.4 GB, Mac only · from huggingface.co/mlx-community
    chat in the terminal
    mlx_lm.chat --model mlx-community/Yi-6B-Chat-4bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model mlx-community/Yi-6B-Chat-4bit

Other ways to run it

  1. Run it as published · needs ~12.12 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-6B-Chat', 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 · LiveBench model judgments (HF… 89%, Open Portuguese LLM Leaderboa… 8%

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
LiveBench model judgments (HF livebench/model_judgment, 2024-10-22 revision) with the LiveBench question datasets1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 89%Apache-2.0 (stated in the LiveBench datasheet)
Open Portuguese LLM Leaderboard (archived)1▇▇ 8%Apache-2.0
prior · pull toward the middle▇ 3%
weighted average= 30.2

$ 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