whatmodel.sh / Baichuan-M1-14B-Instruct

Baichuan-M1-14B-Instruct

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Baichuan-M1-14B-Instruct is an open-weights language model from Baichuan, released January 2025. Its published size is 14.47B parameters.

Catalogue identity notes

Maker repo baichuan-inc/Baichuan-M1-14B-Instruct @c1798df: no licence tag on the card. 14,470,764,320 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2025-01: month the maker's Hugging Face repo was created (createdAt 2025-01-23).

54.0±4.4#349 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 Baichuan-M1-14B-Instruct, out of 1132 with a score, then more from Baichuan.

#modelscorewhereRAMreleased
452Mistral Large 2 (2407) Mistral54±1local~45GB2024-07
453Mistral Small 3.1 24B (2503) Mistral54±1local~17GB2025-03
454Baichuan-M1-14B-Instruct Baichuan54±4local~10GB2025-01
455REBEL-Llama-3-Armo-iter_1 Cornell AGI (REBEL)54±2local~18GB2024-08
456Apertus 70B Instruct (2509) Swiss AI54±6local~22GB2025-09
more from Baichuan
387Baichuan-M2-32B Baichuan56±4local~22GB2025-08
1033Baichuan2-7B-Base Baichuan30±1local~17GB2023-08
1099Baichuan-13B-Chat Baichuan20±3local~29GB2023-07

see all LLMs · find the ones that fit your machine

$ what it's good at

other tests1 not in a task · 91% of its score
testresultvs the others in its rundifficultycountswhy
BRIDGE clinical text, zero-shot average (87 tasks)36.1% ✎▁▁▁▁▁▂▂▃▄▆█▃ beats 50/97hard · avg 32%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 91%blunt · at its level

$ where to run it

LM Studio · recommended

version

  1. Install LM Studio · Open the app once before using the lms command.

    download it (macOS / Windows / Linux) ↗

  2. Run Baichuan-M1-14B-Instruct · needs ~8.1 GB, Mac only · from huggingface.co/mlx-community

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mlx-community/Baichuan-M1-14B-Instruct-4bit -y
  4. Then run lms ls to see its name, and lms chat followed by that name

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 Baichuan-M1-14B-Instruct · needs ~8.1 GB, Mac only · from huggingface.co/mlx-community
    chat in the terminal
    mlx_lm.chat --model mlx-community/Baichuan-M1-14B-Instruct-4bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model mlx-community/Baichuan-M1-14B-Instruct-4bit

Other ways to run it

  1. Run it as published · needs ~28.94 GB · from huggingface.co/baichuan-inc (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='baichuan-inc/Baichuan-M1-14B-Instruct', torch_dtype='auto', device_map='auto', trust_remote_code=True)('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/baichuan-inc ↗

$ how the score was worked out · BRIDGE Medical Leaderboard (H… 91%

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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 91%MIT
prior · pull toward the middle▇▇▇ 9%
weighted average= 54.0

$ 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