[whatmodel.sh](../../README.md) / Yi-Coder 9B Chat
# Yi-Coder 9B Chat
01.AI · released 2024-08 · 8.83B params · apache-2.0
**score 49.6** ±1.3 · #380 of 732 · from 8 benchmark results across 5 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Yi-Coder 9B Chat among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅●█▇▅▄▃▁ #295 of 469 84% 6
coding · #295 of 469 · 84% 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
Aider code editing (133 Exercism … 54.1% ▁·▁▂▄▁●▄█▆▂▂ beats 20/62 medium · avg 51% 35% sharp · at its level
LiveCodeBench official (Jan 1–Nov… 23.5% ▁▁·▁▂●▆█▅▂·▂ beats 8/31 hard · avg 23% 33% sharp
CodeReviewQA: change localisation… 3.9% █▂▁▁●▂▂▃▅▅▅▇ beats 13/47 hard · avg 33% 6.1% sharp · at its level · noisy
CodeReviewQA: solution identifica… 33.0% ▃··▁▂▂▃●█▃▄▂ beats 18/47 medium · avg 51% 5.7% sharp · at its level · noisy
CodeReviewQA: automated code refi… 32.4% ▁▁▁▁▁▁▂▂▃●▅▄ beats 27/47 hard · avg 32% 4.6% at its level
CodeReviewQA: change type recogni… 52.1% ▁▁▁······●▃█ beats 6/47 easy · avg 67% 0.2% blunt · at its level · noisy
other tests (2)
test result vs the others in its run difficulty counts why
EvalPerf correctness (118 tasks, … 78.8% ▂·▂▂▂▆▅▆●▆▃█ beats 15/29 easy · avg 74% 11% at its level
Open PT-LLM Leaderboard average (… 61.9% ▃▁▁▁▁▁▁▁●█▅▁ beats 94/276 medium · avg 72% 4.3% at its level · noisy
source results share of the score
Aider code editing benchmark runs 1 35% ▇▇▇▇
LiveCodeBench official website results 1 33% ▇▇▇▇
CodeReviewQA: The Code Review Comprehen… 4 17% ▇▇
EvalPlus / EvalPerf published evaluatio… 1 11% ▇
Open Portuguese LLM Leaderboard 1 4% ▇
prior: pull toward the middle 1% ▇
weighted average = 49.6
Licences: Aider code editing benchmark runs ([Apache-2.0](../../licenses/aider-polyglot-runs-Apache-2.0.txt)) · LiveCodeBench official website results ([CC-BY-SA-4.0](../../licenses/lcb-sharealike-NOTICE.txt)) · CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-Apache-2.0.txt)) · 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 Yi-Coder 9B Chat (Q4_K_M, needs ~5.3 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Yi-Coder-9B-Chat-GGUF/tree/decf0b18fbef7e812b5764135b9d866a67711197))
```sh
llama cli -hf lmstudio-community/Yi-Coder-9B-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-Coder-9B-Chat-GGUF:Q4_K_M
```
Other versions: Q8_0 (~9.4 GB) `llama cli -hf lmstudio-community/Yi-Coder-9B-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-Coder 9B Chat (Q4_K_M, needs ~5.3 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Yi-Coder-9B-Chat-GGUF/tree/decf0b18fbef7e812b5764135b9d866a67711197))
```sh
lms get https://huggingface.co/lmstudio-community/Yi-Coder-9B-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 (~9.4 GB) `lms get https://huggingface.co/lmstudio-community/Yi-Coder-9B-Chat-GGUF@Q8_0 -y` · 4bit (~5 GB, Mac only) `lms get https://huggingface.co/mlx-community/Yi-Coder-9B-Chat-4bit -y` · 8bit (~9.4 GB, Mac only) `lms get https://huggingface.co/mlx-community/Yi-Coder-9B-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-Coder 9B Chat (Q4_K_M, needs ~5.8 GB, from [ollama.com](https://ollama.com/library/yi-coder/tags))
```sh
ollama run yi-coder:9b-chat-q4_K_M
```
Other versions: Q8_0 (~11.1 GB) `ollama run yi-coder:9b-chat-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-Coder 9B Chat (4bit, needs ~5 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Yi-Coder-9B-Chat-4bit/tree/1f258c856cfb369ef6742f8dea7f22adde02e25d))
```sh
mlx_lm.chat --model mlx-community/Yi-Coder-9B-Chat-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Yi-Coder-9B-Chat-4bit
```
Other versions: 8bit (~9.4 GB, Mac only) `mlx_lm.chat --model mlx-community/Yi-Coder-9B-Chat-8bit`
Model repository and documentation: [huggingface.co/01-ai](https://huggingface.co/01-ai/Yi-Coder-9B-Chat)
[interactive version](https://whatmodel.sh/model/yi-coder-9b-chat) · [JSON](https://whatmodel.sh/api?format=json&all=1)