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StableLM-Tuned-Alpha 7B

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StableLM-Tuned-Alpha 7B is an open-weights language model from Stability AI, released April 2023. Its published size is 7B parameters. Licence: cc-by-nc-sa-4.0.

Catalogue identity notes

Comparison-only. Arena entry 'stablelm-tuned-alpha-7b' (text). LMArena's leaderboard table (arena-leaderboard space @6f2eeeb) labels 'stablelm-tuned-alpha-7b' as 'StableLM-Tuned-Alpha-7B' (Stability AI, CC-BY-NC-SA-4.0), linking https://huggingface.co/stabilityai/stablelm-tuned-alpha-7b. Maker repo stabilityai/stablelm-tuned-alpha-7b @25071b0: licence tag cc-by-nc-sa-4.0. Parameters: nominal size from the card. Released 2023-04: month the maker's Hugging Face repo was created (createdAt 2023-04-19).

14.0±2.0#701 of 719 LLMsfrom 4 benchmark results across 4 sources

$ how it compares

The LLMs ranked either side of StableLM-Tuned-Alpha 7B, out of 828 with a score, then more from Stability AI.

#modelscorewhereRAMreleased
807Gemma 3 270M IT Google15±4local~1.7GB2025-08
808Pythia 1B EleutherAI14±1local~2.2GB2023-02
809StableLM-Tuned-Alpha 7B Stability AI14±2local~6.9GB2023-04
810BLOOM 1.7B BigScience14±1local~3.1GB2022-07
811Flan-T5 Small Google14±3local~1.8GB2022-10
more from Stability AI
718Stable Code 3B Stability AI33±1local~3.3GB2024-01
746StableLM-3B-4E1T Stability AI29±1local~3.3GB2023-09
750StableLM-Base-Alpha 7B v2 Stability AI29±1local~30GB2023-08

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▃▁▁▃▅▄▅█▆▆▇▃ #393 of 393 · 98% of its score · 4 tests
testresultvs the others in its rundifficultycountswhy
Polyrating rating (bias-aware re-fit of Chatbot Arena votes, mid-2024)843.0 ✎▃▁▂▁▃▇█▆▇▄▂▂ beats 0/95avg 1201▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 34%blunt
LMArena (text)953.1 ✎▃▁▁▂▃▃▃▆▆▇█▃ beats 0/379avg 1268▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 34%blunt
MT-Bench (GPT-4 single-answer grading, LMSYS runs)2.7500 MT-Bench score (1-10) ✎▆▂▂▆▆▄██▂▄▄▆ beats 1/28avg 8.819 MT-Bench score (1-10)▇▇▇▇▇▇▇▇▇▇▇▇ 30%sharp · noisy
MixInstruct GPT-Rank (LLM-Blender, mean rank among 11 instruction-tuned LLMs, ChatGPT pairwise judge)8.7100 mean rank (1-11) ✎█···▄··▄···▄ beats 1/4avg 6.444 mean rank (1-11)▇ 0.0%blunt · 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 StableLM-Tuned-Alpha 7B · needs ~5.1 GB · from huggingface.co/mradermacher
    chat in the terminal
    llama cli -hf mradermacher/stablelm-tuned-alpha-7b-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf mradermacher/stablelm-tuned-alpha-7b-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 StableLM-Tuned-Alpha 7B · needs ~5.1 GB · from huggingface.co/mradermacher

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mradermacher/stablelm-tuned-alpha-7b-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 StableLM-Tuned-Alpha 7B · needs ~5.1 GB · from huggingface.co/mradermacher
    chat in the terminal
    ollama run hf.co/mradermacher/stablelm-tuned-alpha-7b-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~31.74 GB · from huggingface.co/stabilityai (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='stabilityai/stablelm-tuned-alpha-7b', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/stabilityai ↗

$ how the score was worked out · Polyrating: A Cost-Effective… 34%, LMArena leaderboard dataset 34%, LMSYS Chatbot Arena leaderboa… 30% and 1 more

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
Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation (arXiv:2409.00696v3)1▇▇▇▇▇▇▇▇▇▇ 34%CC-BY-4.0
LMArena leaderboard dataset1▇▇▇▇▇▇▇▇▇▇ 34%CC BY 4.0
LMSYS Chatbot Arena leaderboard Space: MT-Bench column of the published leaderboard table1▇▇▇▇▇▇▇▇▇ 30%Apache-2.0
LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion (arXiv:2306.02561v3)1▇ 0%CC-BY-4.0
prior · pull toward the middle▇ 2%
weighted average= 14.0

$ 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