[whatmodel.sh](../../README.md) / Yi 1.5 34B Chat
# Yi 1.5 34B Chat
01.AI · released 2024-05 · 34.39B params · apache-2.0
**score 47.2** ±0.7 · #427 of 732 · from 20 benchmark results across 11 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Yi 1.5 34B Chat among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅●█▇▅▄▃▁ #303 of 469 17% 5
chat & writing ▁▁▁▃▆●▆█▆▇▇▃ #306 of 402* 9.2% 2
math ▁▁▃●█▆▄█▄▂▁▁ #342 of 431 18% 3
coding · #303 of 469 · 17% 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), [WizardLM 70B V1.0](wizardlm-70b-v1-0.md), [Yi 1.5 9B Chat](yi-1-5-9b-chat.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
CRUXEval (ZeroEval, 800 problems) 44.1% ▁▂●▃▆█▆▃▂·▁▁ beats 7/47 medium · avg 57% 13% sharp · at its level
CodeReviewQA: solution identifica… 57.0% ▃··▁▂▂▃▂●▃▄▂ beats 32/47 medium · avg 51% 1.5% sharp · at its level · noisy
CodeReviewQA: change localisation… 24.6% █▂▁▁▂▂▂▃●▅▅▇ beats 23/47 hard · avg 33% 1.4% sharp · noisy
CodeReviewQA: automated code refi… 31.1% ▁▁▁▁▁▁▂▂▃●▅▄ beats 24/47 hard · avg 32% 1.1% at its level
CodeReviewQA: change type recogni… 66.1% ▁▁▁······▁●█ beats 15/47 easy · avg 67% 0.0% blunt · at its level · noisy
chat & writing · #306 of 402 · 9.2% 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 [LFM2 350M](lfm2-350m.md), [EXAONE 3.5 2.4B Instruct](exaone-3-5-2-4b.md), [LFM2 700M](lfm2-700m.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1212.9 ▁▁▁▂▃●▃▆▆▇█▃ beats 74/385 avg 1260 4.8% blunt
Polyrating rating (bias-aware re-… 1126.0 ▂▁▂▁▃▇█●▇▄▂▂ beats 59/95 avg 1187 4.4% typical
math · #342 of 431 · 18% 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 (ZeroEval, zero-shot) 84.1% ▁▄·▁▄█●▅▅███ beats 17/43 very easy · avg 87% 7.6% sharp
MATH level 5 25.5% ▂▂▅●▄█▆▃▅▂▃▄ beats 24/92 hard · avg 28% 6.3% sharp
MATH Level 5 (ZeroEval, 721 probl… 18.2% ▁▁▁●█▂▇▇▄··▂ beats 5/38 hard · avg 30% 4.6% sharp
other tests (10)
test result vs the others in its run difficulty counts why
MMLU-Redux (ZeroEval, 2,778 quest… 72.8% ▁·▁▁▄▄●▅█▄▂▁ beats 17/53 easy · avg 75% 23% at its level
BigCodeBench (Instruct) 33.5% ▃▂·▆▆●█▆▅▃▃▃ beats 12/33 medium · avg 39% 5.4% at its level
BigCodeBench (Complete) 43.3% ▂▁▁▄▂▅▇●▆▂▂▂ beats 37/58 medium · avg 48% 5.3% at its level
WildBench v2 WB-Score (task macro) 45.6135 WB-Score ▁·▁▁·▃█▄▇●▅▆ beats 35/52 avg 43.019 WB-Score 5.1% sharp
ZebraLogic (ZeroEval, 1,000 puzzl… 11.5% ▁▁▃●██▁▁·▁▁▁ beats 11/57 hard · avg 15% 4.4% sharp
CPTUB average (SpeakLeash, 0-5 ju… 46.6% ▁▂▂●▃█▃▆▅▄▂▃ beats 14/95 medium · avg 65% 3.8% at its level
GPQA Diamond 32.0% ▁▁▁▁●▄▆▄▅▇█▅ beats 20/211 hard · avg 39% 3.6% sharp
Polish EQ-Bench v2 (SpeakLeash) 46.3% ▁··▁▁▂▂●▅▆█▆ beats 21/70 medium · avg 62% 3.0% at its level
Open PT-LLM Leaderboard average (… 73.1% ▃▁▁▁▁▁▁▁▂●▅▁ beats 203/276 medium · avg 72% 1.2% at its level · noisy
Vectara hallucination leaderboard… 96.3% ▁▁▂▃▄▄█●▃▂▂▁ beats 90/140 very easy · avg 94% 0.3% blunt · maxed out · noisy
source results share of the score
WildEval ZeroEval published results 5 53% ▇▇▇▇▇▇
BigCodeBench paper 2 11% ▇
Epoch AI 2 10% ▇
WildBench v2 leaderboard data 1 5% ▇
LMArena leaderboard dataset 1 5% ▇
Polyrating: A Cost-Effective and Bias-A… 1 4% ▇
CodeReviewQA: The Code Review Comprehen… 4 4% ▇
SpeakLeash CPTUB 1 4% ▇
SpeakLeash Polish EQ-Bench leaderboard 1 3% ▇
Open Portuguese LLM Leaderboard 1 1% ▇
Vectara hallucination leaderboard 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 47.2
Licences: WildEval ZeroEval published results ([Apache-2.0](../../licenses/zeroeval-published-results-Apache-2.0.txt)) · BigCodeBench paper ([CC BY 4.0](http://creativecommons.org/licenses/by/4.0/)) · Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · WildBench v2 leaderboard data ([Apache-2.0](https://github.com/allenai/WildBench/blob/d6b8dcaf377d173d031980f97c16e1a82618c03d/LICENSE)) · 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/)) · SpeakLeash CPTUB ([MIT](../../licenses/hf-leaderboard-spaces-MIT.txt)) · SpeakLeash Polish EQ-Bench leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Vectara hallucination leaderboard ([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 Yi 1.5 34B Chat (Q4_K_M, needs ~20.7 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Yi-1.5-34B-Chat-GGUF/tree/3f7225e3a0dd9ef844d73e559d04448054077625))
```sh
llama cli -hf lmstudio-community/Yi-1.5-34B-Chat-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf lmstudio-community/Yi-1.5-34B-Chat-GGUF:Q4_K_M
```
Other versions: Q8_0 (~36.5 GB) `llama cli -hf lmstudio-community/Yi-1.5-34B-Chat-GGUF:Q8_0`
### 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 Yi 1.5 34B Chat (Q4_K_M, needs ~20.7 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Yi-1.5-34B-Chat-GGUF/tree/3f7225e3a0dd9ef844d73e559d04448054077625))
```sh
lms get https://huggingface.co/lmstudio-community/Yi-1.5-34B-Chat-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~36.5 GB) `lms get https://huggingface.co/lmstudio-community/Yi-1.5-34B-Chat-GGUF@Q8_0 -y` · 4bit (~19.3 GB, Mac only) `lms get https://huggingface.co/mlx-community/Yi-1.5-34B-Chat-4bit -y` · 8bit (~36.5 GB, Mac only) `lms get https://huggingface.co/mlx-community/Yi-1.5-34B-Chat-8bit -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 Yi 1.5 34B Chat (Q4_K_M, needs ~21.13 GB, from [ollama.com](https://ollama.com/library/yi/tags))
```sh
ollama run yi:34b-chat-v1.5-q4_K_M
```
Other versions: Q8_0 (~41.77 GB) `ollama run yi:34b-chat-v1.5-q8_0`
### MLX (Mac)
1. Install MLX (Mac). Apple silicon Macs only, text only.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run Yi 1.5 34B Chat (4bit, needs ~19.3 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Yi-1.5-34B-Chat-4bit/tree/945e3b306ef37c46ab444fdc857d1f3ea7247374))
```sh
mlx_lm.chat --model mlx-community/Yi-1.5-34B-Chat-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Yi-1.5-34B-Chat-4bit
```
Other versions: 8bit (~36.5 GB, Mac only) `mlx_lm.chat --model mlx-community/Yi-1.5-34B-Chat-8bit`
Model repository and documentation: [huggingface.co/01-ai](https://huggingface.co/01-ai/Yi-1.5-34B-Chat)
[interactive version](https://whatmodel.sh/model/yi-1-5-34b-chat) · [JSON](https://whatmodel.sh/api?format=json&all=1)