[whatmodel.sh](../../README.md) / Xwin-LM 70B v0.1 # Xwin-LM 70B v0.1 Xwin-LM · released 2023-09 · 70B params · llama2 **score 45.6** ±1.2 · #461 of 732 · from 7 benchmark results across 2 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at _{● marks Xwin-LM 70B v0.1 among every model on that task or test, worst to best.}

task            vs the others          rank  counts  tests
coding          ▁▁▁▃●▇█▇▅▄▃▁   #401 of 469*    7.5%      1
chat & writing  ▁▁▁▃▆●▆█▆▇▇▃   #300 of 402*     49%      2
math            ▁▁▃▅█●▄█▄▂▁▁   #236 of 431*     11%      1

coding · #401 of 469 · 7.5% of its score

Beats [DeepSeek R1 Distill Qwen 7B](deepseek-r1-distill-qwen-7b.md), [Qwen3.5 2B](qwen3-5-2b.md), [DeepSeek R1 Distill Llama 8B](deepseek-r1-distill-llama-8b.md) here, though they rank higher overall. Trails [Qwen2.5 1.5B Instruct](qwen2-5-1-5b.md), [WizardLM 13B V1.2](wizardlm-13b-v1-2.md), [Falcon3 3B Instruct](falcon3-3b.md) here, though they rank lower overall.

test                       result  vs the others in its run  difficulty      counts  why
Tulu 2: CodexEval pass@10   66.1%  ▃▃▃·▆▃●█▃▂▂▅ beats 13/25  easy · avg 83%    7.5%  sharp · at its level

chat & writing · #300 of 402 · 49% of its score

Beats [Ministral 3 14B Reasoning 2512](ministral-3-14b-reasoning.md), [Gemma 4 E4B](gemma-4-e4b.md), [OpenThinker3 7B](openthinker3-7b.md) here, though they rank higher overall. Trails [EXAONE 3.5 2.4B Instruct](exaone-3-5-2-4b.md), [LFM2 700M](lfm2-700m.md), [Flan-T5 XXL](flan-t5-xxl.md) here, though they rank lower overall.

test                                result  vs the others in its run   difficulty           counts  why
AlpacaEval 2 length-controlled       24.6%  ▁▂▂▃▆▆●▆▄▄▁▁ beats 63/122  medium · avg 35%        47%  sharp
AlpacaEval 1.0 win rate (vs text-…   95.6%  ▂▂·▆·▇▃▆█▆●▂ beats 43/45   very easy · avg 94%    1.6%  sharp · noisy

math · #236 of 431 · 11% of its score

Beats [GPT-4o (2024-08-06)](gpt-4o-2024-08-06.md), [Command A (03-2025)](command-a.md), [GPT-4o (2024-05-13)](gpt-4o-2024-05-13.md) here, though they rank higher overall. Trails [Gemma 7B (base)](gemma-7b-base.md), [CodeGemma 7B (v1.0)](codegemma-7b.md), [Phi-2 (2.7B)](phi-2.md) here, though they rank lower overall.

test   result  vs the others in its run  difficulty      counts  why
GSM8K   65.5%  ▆▂▅█▅▅●▆···▂ beats 20/25  easy · avg 72%     11%  sharp · at its level

other tests (3)

test                                result  vs the others in its run  difficulty        counts  why
BIG-Bench Hard                       65.6%  ▁·····▂█▂●·▁ beats 19/25  easy · avg 74%       21%  sharp · at its level
MMLU                                 65.0%  ▁▁▃▃▄█▄▁●··▁ beats 20/25  medium · avg 73%    9.2%  at its level
Tulu 2: TyDiQA gold passage, 1-sh…   38.2%  ▂··▁●▂▂▂█▅▁▂ beats 5/25   medium · avg 59%    2.1%  blunt · at its level

## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's 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 is added up by who ran the tests. [Method](../method.md)

source                               results  share of the score
Tulu 2                                     5   50% ▇▇▇▇▇▇
AlpacaEval 1.0 and 2.0 leaderboards        2   49% ▇▇▇▇▇▇
prior: pull toward the middle                   1% ▇
weighted average                              = 45.6

_{Licences: Tulu 2 ([CC BY 4.0](../../licenses/CC-BY-4.0.txt)) · AlpacaEval 1.0 and 2.0 leaderboards ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0))} ## How to run it ### llama.cpp (recommended) 1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md) ```sh # macOS / Linux curl -LsSf https://llama.app/install.sh | sh # Windows irm https://llama.app/install.ps1 | iex ``` 2. Run Xwin-LM 70B v0.1 (Q4_K_M, needs ~41.4 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/Xwin-LM-70B-V0.1-GGUF/tree/5f9be9552236a08b7af62b8149cc01761b6dfee2)) ```sh 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) ```sh llama serve -hf mradermacher/Xwin-LM-70B-V0.1-GGUF:Q4_K_M ``` Other versions: Q2_K (~25.5 GB) `llama cli -hf mradermacher/Xwin-LM-70B-V0.1-GGUF:Q2_K` ### LM Studio 1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download) 2. Run Xwin-LM 70B v0.1 (Q4_K_M, needs ~41.4 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/Xwin-LM-70B-V0.1-GGUF/tree/5f9be9552236a08b7af62b8149cc01761b6dfee2)) ```sh lms get https://huggingface.co/mradermacher/Xwin-LM-70B-V0.1-GGUF@Q4_K_M -y ``` 3. Then run lms ls to see its name, and lms chat followed by that name Other versions: Q2_K (~25.5 GB) `lms get https://huggingface.co/mradermacher/Xwin-LM-70B-V0.1-GGUF@Q2_K -y` ### Ollama 1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download) ```sh # Linux curl -fsSL https://ollama.com/install.sh | sh ``` 2. Run Xwin-LM 70B v0.1 (Q4_0, needs ~38.87 GB, from [ollama.com](https://ollama.com/library/xwinlm/tags)) ```sh ollama run xwinlm:70b-v0.1 ``` Model repository and documentation: [huggingface.co/Xwin-LM](https://huggingface.co/Xwin-LM/Xwin-LM-70B-V0.1) [interactive version](https://whatmodel.sh/model/xwin-lm-70b-v0-1) · [JSON](https://whatmodel.sh/api?format=json&all=1)