[whatmodel.sh](../../README.md) / Bielik 11B v2.3 Instruct
# Bielik 11B v2.3 Instruct
SpeakLeash · released 2024-08 · 11.17B params · apache-2.0
**score 50.9** ±2.0 · #352 of 732 · from 6 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 Bielik 11B v2.3 Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁●▆█▄▅▅▄▄▂▁ #294 of 313* 28% 2
agent tasks · #294 of 313 · 28% of its score
Trails [Granite 4.0 H 350M](granite-4-0-h-350m.md), [Qwen3 0.6B](qwen3-0-6b.md), [Qwen2.5 1.5B Instruct](qwen2-5-1-5b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
BFCL V4 (December 2025 archive) 21.9% ▁▁▁▂●█▅▅▆▂▂▂ beats 7/77 hard · avg 31% 18% at its level
BFCL v3 40.8% ▁▁▁▁▁●▅█▅▄▁▁ beats 16/114 medium · avg 51% 10% blunt · at its level
other tests (4)
test result vs the others in its run difficulty counts why
CPTUB average (SpeakLeash, 0-5 ju… 72.6% ▁▂▂▂▃█▃●▅▄▂▃ beats 60/95 medium · avg 65% 31% at its level
Polish EQ-Bench v2 (SpeakLeash) (… 70.9% ▁··▁▁▂▂▆▅▆●▆ beats 54/70 medium · avg 62% 25% at its level
Open PT-LLM Leaderboard average (… 73.1% ▃▁▁▁▁▁▁▁▂●▅▁ beats 202/276 medium · avg 72% 10% at its level · noisy
EuroEval Life in the UK (English … 83.8% ▁▁▁▁▁▁▁▂▄●▄▃ beats 91/146 easy · avg 82% 5.0% noisy
source results share of the score
SpeakLeash CPTUB (submitted / maker-rep… 1 31% ▇▇▇▇
Berkeley Function Calling Leaderboard r… 2 28% ▇▇▇
SpeakLeash Polish EQ-Bench leaderboard … 1 25% ▇▇▇
Open Portuguese LLM Leaderboard 1 10% ▇
EuroEval leaderboards 1 5% ▇
prior: pull toward the middle 2% ▇
weighted average = 50.9
Licences: SpeakLeash CPTUB ([MIT](../../licenses/hf-leaderboard-spaces-MIT.txt)) · Berkeley Function Calling Leaderboard result archive ([Apache-2.0](../../licenses/bfcl-result-archive-v4-Apache-2.0.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)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE))
## 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 Bielik 11B v2.3 Instruct (Q4_K_M, needs ~6.72 GB, from [huggingface.co/speakleash](https://huggingface.co/speakleash/Bielik-11B-v2.3-Instruct-GGUF/tree/b6b2f467358d9eca16fddcc2f0fd506ea3e87858))
```sh
llama cli -hf speakleash/Bielik-11B-v2.3-Instruct-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf speakleash/Bielik-11B-v2.3-Instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~11.87 GB) `llama cli -hf speakleash/Bielik-11B-v2.3-Instruct-GGUF`
### 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 Bielik 11B v2.3 Instruct (Q4_K_M, needs ~6.72 GB, from [huggingface.co/speakleash](https://huggingface.co/speakleash/Bielik-11B-v2.3-Instruct-GGUF/tree/b6b2f467358d9eca16fddcc2f0fd506ea3e87858))
```sh
lms get https://huggingface.co/speakleash/Bielik-11B-v2.3-Instruct-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 (~11.87 GB) `lms get https://huggingface.co/speakleash/Bielik-11B-v2.3-Instruct-GGUF -y` · 8bit (~11.9 GB, Mac only) `lms get https://huggingface.co/lknik/Bielik-11B-v2.3-Instruct-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 Bielik 11B v2.3 Instruct (Q4_K_M, needs ~6.72 GB, from [huggingface.co/speakleash](https://huggingface.co/speakleash/Bielik-11B-v2.3-Instruct-GGUF/tree/b6b2f467358d9eca16fddcc2f0fd506ea3e87858))
```sh
ollama run hf.co/speakleash/Bielik-11B-v2.3-Instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~11.87 GB) `ollama run hf.co/speakleash/Bielik-11B-v2.3-Instruct-GGUF`
### 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 Bielik 11B v2.3 Instruct (8bit, needs ~11.9 GB, Mac only, from [huggingface.co/lknik](https://huggingface.co/lknik/Bielik-11B-v2.3-Instruct-8bit/tree/0b58b4ba6c082741b474b03e80ab047b196564c8))
```sh
mlx_lm.chat --model lknik/Bielik-11B-v2.3-Instruct-8bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model lknik/Bielik-11B-v2.3-Instruct-8bit
```
Model repository and documentation: [huggingface.co/speakleash](https://huggingface.co/speakleash/Bielik-11B-v2.3-Instruct)
[interactive version](https://whatmodel.sh/model/bielik-11b-v2-3-instruct) · [JSON](https://whatmodel.sh/api?format=json&all=1)