[whatmodel.sh](../../README.md) / Yi-1.5 34B (base)
# Yi-1.5 34B (base)
01.AI · released 2024-05 · 34.39B params · apache-2.0
**score 48.6** ±2.3 · #397 of 732 · from 4 benchmark results across 3 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 (base) among every model on that task or test, worst to best.
other tests (4)
test result vs the others in its run difficulty counts why
BigCodeBench (Complete) 40.0% ▂▁▁▄▂▅●█▆▂▂▂ beats 28/58 medium · avg 48% 65% at its level
Open PT-LLM Leaderboard average (… 72.0% ▃▁▁▁▁▁▁▁▂●▅▁ beats 191/276 medium · avg 72% 14% at its level · noisy
EuroEval HellaSwag (English), tes… 95.9% ▃·▁▁▂▁▂▅█▇▄● beats 100/100 medium · avg 67% 11% sharp · at its level · noisy
EuroEval Life in the UK (English … 88.3% ▁▁▁▁▁▁▁▂▄█●▃ beats 112/146 easy · avg 82% 7.6% at its level · noisy
source results share of the score
BigCodeBench paper 1 65% ▇▇▇▇▇▇▇▇
EuroEval leaderboards 2 19% ▇▇
Open Portuguese LLM Leaderboard 1 14% ▇▇
prior: pull toward the middle 2% ▇
weighted average = 48.6
Licences: BigCodeBench paper ([CC BY 4.0](http://creativecommons.org/licenses/by/4.0/)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE)) · 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-1.5 34B (base) (Q4_K_M, needs ~20.7 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/Yi-1.5-34B-GGUF/tree/05df8c59d9896d18289be5ea28c758f667fe3726))
```sh
llama cli -hf mradermacher/Yi-1.5-34B-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf mradermacher/Yi-1.5-34B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~36.5 GB) `llama cli -hf mradermacher/Yi-1.5-34B-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 (base) (Q4_K_M, needs ~20.7 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/Yi-1.5-34B-GGUF/tree/05df8c59d9896d18289be5ea28c758f667fe3726))
```sh
lms get https://huggingface.co/mradermacher/Yi-1.5-34B-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/mradermacher/Yi-1.5-34B-GGUF@Q8_0 -y` · 4bit (~19.3 GB, Mac only) `lms get https://huggingface.co/mlx-community/Yi-1.5-34B-4bit -y` · 8bit (~36.5 GB, Mac only) `lms get https://huggingface.co/mlx-community/Yi-1.5-34B-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 (base) (Q4_K_M, needs ~20.7 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/Yi-1.5-34B-GGUF/tree/05df8c59d9896d18289be5ea28c758f667fe3726))
```sh
ollama run hf.co/mradermacher/Yi-1.5-34B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~36.5 GB) `ollama run hf.co/mradermacher/Yi-1.5-34B-GGUF: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 (base) (4bit, needs ~19.3 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Yi-1.5-34B-4bit/tree/2ef9128b5a08bd48fa3a75412ff4b5b7ead9438c))
```sh
mlx_lm.chat --model mlx-community/Yi-1.5-34B-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Yi-1.5-34B-4bit
```
Other versions: 8bit (~36.5 GB, Mac only) `mlx_lm.chat --model mlx-community/Yi-1.5-34B-8bit`
### Other ways to run it
1. Run it as published (BF16, needs ~68.78 GB, from [huggingface.co/01-ai](https://huggingface.co/01-ai/Yi-1.5-34B/tree/58a29f6dca2a4eda38821c086c29c1906114aabb))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='01-ai/Yi-1.5-34B', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/01-ai](https://huggingface.co/01-ai/Yi-1.5-34B)
[interactive version](https://whatmodel.sh/model/yi-1-5-34b-base) · [JSON](https://whatmodel.sh/api?format=json&all=1)