whatmodel.sh / Xwin-LM 70B v0.1

Xwin-LM 70B v0.1

+ contribute
Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Xwin-LM 70B v0.1 is an open-weights language model from Xwin-LM, released September 2023. Its published size is 70B parameters. Licence: llama2.

Catalogue identity notes

Exact full-precision model evaluated in the Tulu 2 paper (arXiv:2311.10702v2). QLoRA ablations are separate and not imported.

Model information sources

Release date ↗ · Weights licence ↗

43.9±1.2#463 of 719 LLMsfrom 7 benchmark results across 2 sources

$ how it compares

The LLMs ranked either side of Xwin-LM 70B v0.1, out of 828 with a score, then more from Xwin-LM.

#modelscorewhereRAMreleased
550Qwen2 7B Instruct Alibaba44±1local~6GB2024-06
551Falcon3 7B Instruct TII44±3local~5.9GB2024-11
552Xwin-LM 70B v0.1 Xwin-LM44±1local~28GB2023-09
553Qwen3-VL 2B Instruct Alibaba44±1local~2.7GB2025-10
554Dolphin 2.6 Mixtral 8x7B Cognitive Computations44±1local~29GB2023-12
more from Xwin-LM
450XwinCoder-34B Xwin-LM48±1local~23GB2023-11

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▆██▇▆▄▃▁ #392 of 458 * · 8.9% of its score · 1 test

Beats DeepSeek R1 Distill Qwen 7B, Qwen3.5 2B, DeepSeek R1 Distill Llama 8B here, though they rank higher overall. Trails Qwen2.5 1.5B Instruct, WizardLM 13B V1.2, Falcon3 3B Instruct here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Tulu 2: CodexEval pass@1066.1% ✎▃▃▃·▆▃▃█▃▂▂▅ beats 13/25very easy · avg 86%▇▇▇▇ 8.9%sharp · at its level
chat & writing▁▁▁▃▅▄▅█▆▆▇▃ #293 of 393 * · 49% of its score · 2 tests

Beats Ministral 3 14B Reasoning 2512, Gemma 4 E4B, OpenThinker3 7B here, though they rank higher overall. Trails Flan-T5 XXL, LFM2 700M, EXAONE 3.5 2.4B Instruct here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
AlpacaEval 2 length-controlled24.6% ✎▁▂▂▃▆▆█▆▄▄▁▁ beats 63/122medium · avg 39%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 46%typical
AlpacaEval 1.0 win rate (vs text-davinci-003, GPT-4 judge)95.6% ✎▂▂·▆·▇▃▆█▆▄▂ beats 43/45very easy · avg 96%▇ 2.7%sharp · noisy
math▁▂▃▅▆▅▄█▄▁▁▁ #263 of 398 * · 7.4% of its score · 1 test

Beats Molmo 2 8B, Llama 3.3 70B Instruct, DeepSeek-V2.5 here, though they rank higher overall. Trails DeepSeek R1 Distill Qwen 1.5B, Mixtral 8x7B v0.1 (base), InternVL2-4B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
GSM8K65.5% ✎▆▂▅█▅▅▃▆···▂ beats 20/25easy · avg 73%▇▇▇ 7.4%sharp · at its level
other tests3 not in a task · 34% of its score
testresultvs the others in its rundifficultycountswhy
BIG-Bench Hard65.6% ✎▁·····▂█▂▅·▁ beats 19/25easy · avg 77%▇▇▇▇▇▇▇▇▇ 23%sharp · at its level
MMLU65.0% ✎▁▁▃▃▄█▄▁█··▁ beats 20/25easy · avg 74%▇▇▇ 8.4%at its level
Tulu 2: TyDiQA gold passage, 1-shot F138.2% ✎▂··▁▃▂▂▂█▅▁▂ beats 5/25medium · avg 61%▇ 2.5%blunt · at its level

$ 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 Xwin-LM 70B v0.1 · needs ~41.4 GB · from huggingface.co/mradermacher
    chat in the terminal
    llama cli -hf mradermacher/Xwin-LM-70B-V0.1-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf mradermacher/Xwin-LM-70B-V0.1-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 Xwin-LM 70B v0.1 · needs ~41.4 GB · from huggingface.co/mradermacher

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mradermacher/Xwin-LM-70B-V0.1-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 Xwin-LM 70B v0.1 · needs ~38.87 GB · from ollama.com
    chat in the terminal
    ollama run xwinlm:70b-v0.1

Official weights and documentation: huggingface.co/Xwin-LM ↗

$ how the score was worked out · Tulu 2 — base and instruction… 50%, AlpacaEval 1.0 and 2.0 leader… 49%

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
Tulu 2 — base and instruction-tuned evaluations5▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 50%CC BY 4.0
AlpacaEval 1.0 and 2.0 leaderboards (tatsu-lab/alpaca_eval repository)2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 49%Apache-2.0
prior · pull toward the middle▇ 1%
weighted average= 43.9

$ 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