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Stable LM 2 1.6B Chat

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Stable LM 2 1.6B Chat is an open-weights language model from Stability AI, released April 2024. Its published size is 1.64B parameters and a 4k-token context window. Licence: other.

Catalogue identity notes

stabilityai/stablelm-2-1_6b-chat @f3fe670 (created 2024-04-08, licence other). Independent row: Open PT-LLM (Portuguese) leaderboard average.

Model information sources

Release date ↗ · Weights licence ↗

HF safetensors metadata 1,644,515,328 parameters.

36.3±6.7#783 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 Stable LM 2 1.6B Chat, out of 1132 with a score, then more from Stability AI.

#modelscorewhereRAMreleased
946Falcon3 3B Instruct TII37±3local~3.4GB2024-12
947Zephyr 7B alpha Hugging Face36±1local~6.1GB2023-10
948Stable LM 2 1.6B Chat Stability AI36±7local~2.5GB2024-04
949Vicuna 13B v1.5 LMSYS36±2local~9.8GB2023-08
950Granite 3.1 2B Instruct IBM36±1local~3.1GB2024-12
more from Stability AI
661Stable LM 2 12B Chat Stability AI47±6local~8.9GB2024-04
879Stable LM 2 Zephyr 1.6B Stability AI40±7local~2.5GB2024-01
898StableLM Zephyr 3B Stability AI39±7local~3.2GB2023-11

see all LLMs · find the ones that fit your machine

$ what it's good at

other tests1 not in a task · 80% of its score
testresultvs the others in its rundifficultycountswhy
Open PT-LLM Leaderboard average (9 Portuguese tasks)50.6% ✎▃▁▁▁▁▁▁▃▂█▅▁ beats 70/276medium · avg 73%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 80%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 Stable LM 2 1.6B Chat · needs ~1 GB · from huggingface.co/mradermacher
    chat in the terminal
    llama cli -hf mradermacher/stablelm-2-1_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/stablelm-2-1_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 Stable LM 2 1.6B Chat · needs ~1 GB · from huggingface.co/mradermacher

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mradermacher/stablelm-2-1_6b-chat-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run Stable LM 2 1.6B Chat · needs ~0.983 GB · from ollama.com
    chat in the terminal
    ollama run stablelm2:1.6b

MLX (Mac)

  1. Install MLX (Mac) · Apple silicon Macs only, text only.
    with uv
    uv tool install mlx-lm
    or
    pip install mlx-lm
  2. Run Stable LM 2 1.6B Chat · needs ~1.2 GB, Mac only · from huggingface.co/mlx-community
    chat in the terminal
    mlx_lm.chat --model mlx-community/stablelm-2-1_6b-chat-4bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model mlx-community/stablelm-2-1_6b-chat-4bit

Official weights and documentation: huggingface.co/stabilityai ↗

$ how the score was worked out · Open Portuguese LLM Leaderboa… 80%

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
Open Portuguese LLM Leaderboard (archived)1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 80%Apache-2.0
prior · pull toward the middle▇▇▇▇▇▇ 20%
weighted average= 36.3

$ 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