[whatmodel.sh](../../README.md) / Yi 34B Chat # Yi 34B Chat 01.AI · released 2023-11 · 34.39B params · apache-2.0 **score 44.9** ±1.0 · #479 of 732 · from 8 benchmark results across 7 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks Yi 34B Chat among every model on that task or test, worst to best. 
task            vs the others          rank  counts  tests
chat & writing  ▁▁▁▃●▄▆█▆▇▇▃    #319 of 402     81%      5
math            ▁▁●▅█▆▄█▄▂▁▁   #397 of 431*     11%      1
chat & writing · #319 of 402 · 81% 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       27.2%  ▁▂▂▃▆▆●▆▄▄▁▁ beats 72/122  medium · avg 35%        36%  sharp
Arena-Hard (v0.1, GPT-4-1106 judg…   23.1%  ▂▁▁▅●▅▄▃▃▅▃▁ beats 19/60   medium · avg 47%        22%  sharp · at its level
LMArena (text)                      1183.8  ▁▁▁▂●▃▃▆▆▇█▃ beats 61/385  avg 1260                11%  blunt
Polyrating rating (bias-aware re-…  1101.0  ▂▁▂▁▃▇●▆▇▄▂▂ beats 46/95   avg 1187                10%  typical
AlpacaEval 1.0 win rate (vs text-…   94.1%  ▂▂·▆·▇▃▆█●▄▂ beats 39/45   very easy · avg 94%    1.3%  sharp · noisy
math · #397 of 431 · 11% of its score Trails [GPT-Neo 125M](gpt-neo-125m.md), [OPT-125M](opt-125m.md), [GPT-2 XL (1.5B)](gpt-2-xl.md) here, though they rank lower overall. 
test          result  vs the others in its run  difficulty      counts  why
MATH level 5    5.1%  ●▂▅▆▄█▆▃▅▂▃▄ beats 2/92   hard · avg 28%     11%  sharp
other tests (3) 
test                                result  vs the others in its run    difficulty        counts  why
EQ-Bench v1 (60 questions, paper …   51.0%  ▂··▄·▂▆▄▂●·▂ beats 11/14    medium · avg 53%    5.0%  at its level
Open PT-LLM Leaderboard average (…   70.8%  ▃▁▁▁▁▁▁▁▂●▅▁ beats 173/276  medium · avg 72%    2.7%  at its level · noisy
GPQA Diamond                         14.7%  ▁▁▁▁▂▄▅▅███▄ beats 0/322    hard · avg 39%         —  failed run: Well below the 25% guess ra…
## 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
AlpacaEval 1.0 and 2.0 leaderboards             2   37% ▇▇▇▇
Arena-Hard-Auto v0.1 leaderboard                1   22% ▇▇▇
LMArena leaderboard dataset                     1   11% ▇
Epoch AI                                        1   11% ▇
Polyrating: A Cost-Effective and Bias-A…        1   10% ▇
EQ-Bench: An Emotional Intelligence Ben…        1    5% ▇
Open Portuguese LLM Leaderboard                 1    3% ▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 44.9
Licences: AlpacaEval 1.0 and 2.0 leaderboards ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · Arena-Hard-Auto v0.1 leaderboard ([Apache-2.0](../../licenses/arena-hard-auto-v0-1-Apache-2.0.txt)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Epoch AI ([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/)) · EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models ([CC BY 4.0](../../licenses/paper-2312.06281v2-CC-BY-4.0.txt)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt))
Not counted (1) 
source    test          score  why
Epoch AI  GPQA Diamond  14.7%  failed run: Well below the 25% guess rate. Peers of similar age and size score 0.25 to 0.27 in the same Epoch run (deepseek-llm-67b-chat 0.246, Llama-2-70b-chat 0.263), which suggests answer-format failures.
## 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 34B Chat (Q4_K_M, needs ~20.7 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/Yi-34B-Chat-GGUF/tree/ee827f906e733381ab65c650dcfdd2caf6de8762)) ```sh llama cli -hf TheBloke/Yi-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 TheBloke/Yi-34B-Chat-GGUF:Q4_K_M ``` Other versions: Q8_0 (~36.5 GB) `llama cli -hf TheBloke/Yi-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 34B Chat (Q4_K_M, needs ~20.7 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/Yi-34B-Chat-GGUF/tree/ee827f906e733381ab65c650dcfdd2caf6de8762)) ```sh lms get https://huggingface.co/TheBloke/Yi-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/TheBloke/Yi-34B-Chat-GGUF@Q8_0 -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 34B Chat (Q4_K_M, needs ~21.13 GB, from [ollama.com](https://ollama.com/library/yi/tags)) ```sh ollama run yi:34b-chat-q4_K_M ``` Other versions: Q8_0 (~41.77 GB) `ollama run yi:34b-chat-q8_0` Model repository and documentation: [huggingface.co/01-ai](https://huggingface.co/01-ai/Yi-34B-Chat) [interactive version](https://whatmodel.sh/model/yi-34b-chat) · [JSON](https://whatmodel.sh/api?format=json&all=1)