[whatmodel.sh](../../README.md) / Yi 34B 200K (base)
# Yi 34B 200K (base)
01.AI · released 2023-11 · 34.39B params · apache-2.0
**score 50.4** ±4.2 · #364 of 732 · from 3 benchmark results across 2 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 200K (base) among every model on that task or test, worst to best.
other tests (3)
test result vs the others in its run difficulty counts why
Open PT-LLM Leaderboard average (… 68.8% ▃▁▁▁▁▁▁▁▂●▅▁ beats 156/276 medium · avg 72% 42% at its level · noisy
RULER 64k 83.2% ▁▁·▁·▂▃●█▂·▁ beats 9/22 easy · avg 82% 37% typical
RULER 128k 77.3% ▄··▂▄▂▆█●▄▄▂ beats 16/21 easy · avg 67% 13% at its level · noisy
source results share of the score
NVIDIA RULER repository README results … 2 50% ▇▇▇▇▇▇
Open Portuguese LLM Leaderboard 1 42% ▇▇▇▇▇
prior: pull toward the middle 8% ▇
weighted average = 50.4
Licences: NVIDIA RULER repository README results table ([Apache-2.0](https://github.com/NVIDIA/RULER/blob/c3f5e3b4f87f97e048793bb510a3a6b19a46bf3a/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 34B 200K (base) (Q4_K_M, needs ~20.7 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/Yi-34B-200K-GGUF/tree/0e5be0d99e4f9636c795d4bd11bbf7ee3b452536))
```sh
llama cli -hf TheBloke/Yi-34B-200K-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-200K-GGUF:Q4_K_M
```
Other versions: Q8_0 (~36.5 GB) `llama cli -hf TheBloke/Yi-34B-200K-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 200K (base) (Q4_K_M, needs ~20.7 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/Yi-34B-200K-GGUF/tree/0e5be0d99e4f9636c795d4bd11bbf7ee3b452536))
```sh
lms get https://huggingface.co/TheBloke/Yi-34B-200K-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-200K-GGUF@Q8_0 -y` · 8bit (~36.5 GB, Mac only) `lms get https://huggingface.co/Fmuaddib/Yi-34B-200K-mlx-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 34B 200K (base) (Q4_K_M, needs ~20.7 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/Yi-34B-200K-GGUF/tree/0e5be0d99e4f9636c795d4bd11bbf7ee3b452536))
```sh
ollama run hf.co/TheBloke/Yi-34B-200K-GGUF:Q4_K_M
```
Other versions: Q8_0 (~36.5 GB) `ollama run hf.co/TheBloke/Yi-34B-200K-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 34B 200K (base) (8bit, needs ~36.5 GB, Mac only, from [huggingface.co/Fmuaddib](https://huggingface.co/Fmuaddib/Yi-34B-200K-mlx-8Bit/tree/5480038b2192c6972fbd48be1d6868a2400db4c6))
```sh
mlx_lm.chat --model Fmuaddib/Yi-34B-200K-mlx-8Bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model Fmuaddib/Yi-34B-200K-mlx-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-34B-200K/tree/e0ae1afac6b69f604556efd441ab4befafb2a835))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='01-ai/Yi-34B-200K', 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-34B-200K)
[interactive version](https://whatmodel.sh/model/yi-34b-200k) · [JSON](https://whatmodel.sh/api?format=json&all=1)