[whatmodel.sh](../../README.md) / WizardLM 70B V1.0
# WizardLM 70B V1.0
Microsoft · released 2023-08 · 70B params · llama2
**score 43.9** ±1.0 · #503 of 732 · from 14 benchmark results across 6 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks WizardLM 70B V1.0 among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅▇●▇▅▄▃▁ #294 of 469 7.4% 4
chat & writing ▁▁▁▃●▄▆█▆▇▇▃ #322 of 402 51% 3
math ▁▁▃●█▆▄█▄▂▁▁ #343 of 431* 8.3% 1
coding · #294 of 469 · 7.4% of its score
Beats [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md), [Qwen3-VL 30B-A3B Instruct](qwen3-vl-30b-a3b-instruct.md) here, though they rank higher overall. Trails [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
CodeReviewQA: solution identifica… 49.0% ▃··▁▂▂▃▂●▃▄▂ beats 24/47 medium · avg 51% 2.8% sharp · at its level · noisy
CodeReviewQA: change localisation… 19.2% █▂▁▁▂▂▂●▅▅▅▇ beats 19/47 hard · avg 33% 2.3% sharp · noisy
CodeReviewQA: automated code refi… 30.9% ▁▁▁▁▁▁▂▂▃●▅▄ beats 23/47 hard · avg 32% 2.2% typical
CodeReviewQA: change type recogni… 78.6% ▁▁▁······▁▃● beats 41/47 easy · avg 67% 0.1% blunt · at its level · noisy
chat & writing · #322 of 402 · 51% of its score
Beats [Gemma 4 E4B](gemma-4-e4b.md), [OpenThinker3 7B](openthinker3-7b.md), [Code Llama 70B Instruct](codellama-70b-instruct.md) here, though they rank higher overall. Trails [LFM2 350M](lfm2-350m.md), [Gemma 3 1B](gemma-3-1b.md), [Qwen3 0.6B](qwen3-0-6b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AlpacaEval 2 length-controlled 17.6% ▁▂▂▃▆●█▆▄▄▁▁ beats 45/122 medium · avg 35% 31% sharp
LMArena (text) 1184.8 ▁▁▁▂●▃▃▆▆▇█▃ beats 62/385 avg 1260 11% blunt
Polyrating rating (bias-aware re-… 1129.0 ▂▁▂▁▃▇█●▇▄▂▂ beats 62/95 avg 1187 10% typical
math · #343 of 431 · 8.3% of its score
Beats [Athene-70B](athene-70b.md), [Hermes 2 Theta Llama-3 70B](hermes-2-theta-llama-3-70b.md), [Gemma 2 9B IT](gemma-2-9b.md) here, though they rank higher overall. Trails [Gemma 2B (base)](gemma-2b-base.md), [Stable Code 3B](stable-code-3b.md), [Zephyr 7B beta](zephyr-7b-beta.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K 73.2% ▂▂▂▂▃▄▅▇●█▆▄ beats 125/206 easy · avg 80% 8.3% sharp · at its level
other tests (6)
test result vs the others in its run difficulty counts why
BIG-Bench Hard 51.1% ▁▁▁▁▂▄▅●█▃▃▁ beats 68/155 medium · avg 63% 12% sharp · at its level
MMLU 55.0% ▁▁▁▁▁▂▂●██▆▄ beats 65/214 medium · avg 72% 6.3% at its level
ARC Challenge 71.9% ▄▁▁▂▁▄█▇▃●▃▂ beats 121/140 easy · avg 82% 6.2% sharp · at its level
AGIEval English 48.7% █▄●··▄··▄█·█ beats 3/9 easy · avg 65% 4.5% at its level
Open PT-LLM Leaderboard average (… 71.1% ▃▁▁▁▁▁▁▁▂●▅▁ beats 180/276 medium · avg 72% 2.5% at its level · noisy
ARC Easy 80.7% ▁▃▂▄▆█▅●▁··▁ beats 92/100 easy · avg 85% 1.4% blunt
source results share of the score
Orca 2: Teaching Small Language Models … 6 38% ▇▇▇▇▇
AlpacaEval 1.0 and 2.0 leaderboards 1 31% ▇▇▇▇
LMArena leaderboard dataset 1 11% ▇
Polyrating: A Cost-Effective and Bias-A… 1 10% ▇
CodeReviewQA: The Code Review Comprehen… 4 7% ▇
Open Portuguese LLM Leaderboard 1 3% ▇
prior: pull toward the middle 0% ▇
weighted average = 43.9
Licences: Orca 2: Teaching Small Language Models How to Reason ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · AlpacaEval 1.0 and 2.0 leaderboards ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt))
## 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 WizardLM 70B V1.0 (Q4_K_M, needs ~41.4 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/WizardLM-70B-V1.0-GGUF/tree/173fd6b2eead0ef9075391f3b9c8536b2c87ff03))
```sh
llama cli -hf TheBloke/WizardLM-70B-V1.0-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf TheBloke/WizardLM-70B-V1.0-GGUF:Q4_K_M
```
Other versions: Q2_K (~29.3 GB) `llama cli -hf TheBloke/WizardLM-70B-V1.0-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 WizardLM 70B V1.0 (Q4_K_M, needs ~41.4 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/WizardLM-70B-V1.0-GGUF/tree/173fd6b2eead0ef9075391f3b9c8536b2c87ff03))
```sh
lms get https://huggingface.co/TheBloke/WizardLM-70B-V1.0-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 (~29.3 GB) `lms get https://huggingface.co/TheBloke/WizardLM-70B-V1.0-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 WizardLM 70B V1.0 (Q4_K_M, needs ~41.42 GB, from [ollama.com](https://ollama.com/library/wizardlm/tags))
```sh
ollama run wizardlm:70b-llama2-q4_K_M
```
Other versions: Q4_0 (~38.87 GB) `ollama run wizardlm:70b-llama2-q4_0`
Model repository and documentation: [huggingface.co/WizardLMTeam](https://huggingface.co/WizardLMTeam/WizardLM-70B-V1.0)
[interactive version](https://whatmodel.sh/model/wizardlm-70b-v1-0) · [JSON](https://whatmodel.sh/api?format=json&all=1)