[whatmodel.sh](../../README.md) / Bielik 11B v2.1 Instruct
# Bielik 11B v2.1 Instruct
SpeakLeash · released 2024-08 · 11.17B params · apache-2.0
**score 53.2** ±2.5 · #315 of 732 · from 3 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 Bielik 11B v2.1 Instruct 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
CPTUB average (SpeakLeash, 0-5 ju… 72.2% ▁▂▂▂▃█▃●▅▄▂▃ beats 58/95 medium · avg 65% 46% at its level
Polish EQ-Bench v2 (SpeakLeash) (… 66.3% ▁··▁▁▂▂▆▅▆●▆ beats 45/70 medium · avg 62% 36% at its level
Open PT-LLM Leaderboard average (… 73.5% ▃▁▁▁▁▁▁▁▂█●▁ beats 208/276 medium · avg 72% 15% at its level · noisy
source results share of the score
SpeakLeash CPTUB (submitted / maker-rep… 1 46% ▇▇▇▇▇▇
SpeakLeash Polish EQ-Bench leaderboard … 1 36% ▇▇▇▇
Open Portuguese LLM Leaderboard 1 15% ▇▇
prior: pull toward the middle 3% ▇
weighted average = 53.2
Licences: SpeakLeash CPTUB ([MIT](../../licenses/hf-leaderboard-spaces-MIT.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))
## 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 v2.1 Instruct (Q4_K_M, needs ~6.7 GB, from [huggingface.co/speakleash](https://huggingface.co/speakleash/Bielik-11B-v2.1-Instruct-GGUF/tree/e5be66e3deaf548c3dfe0ce176175604c90ebb97))
```sh
llama cli -hf speakleash/Bielik-11B-v2.1-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-v2.1-Instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~11.9 GB) `llama cli -hf speakleash/Bielik-11B-v2.1-Instruct-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 Bielik 11B v2.1 Instruct (Q4_K_M, needs ~6.7 GB, from [huggingface.co/speakleash](https://huggingface.co/speakleash/Bielik-11B-v2.1-Instruct-GGUF/tree/e5be66e3deaf548c3dfe0ce176175604c90ebb97))
```sh
lms get https://huggingface.co/speakleash/Bielik-11B-v2.1-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 (~11.9 GB) `lms get https://huggingface.co/speakleash/Bielik-11B-v2.1-Instruct-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 Bielik 11B v2.1 Instruct (Q4_K_M, needs ~6.7 GB, from [huggingface.co/speakleash](https://huggingface.co/speakleash/Bielik-11B-v2.1-Instruct-GGUF/tree/e5be66e3deaf548c3dfe0ce176175604c90ebb97))
```sh
ollama run hf.co/speakleash/Bielik-11B-v2.1-Instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~11.9 GB) `ollama run hf.co/speakleash/Bielik-11B-v2.1-Instruct-GGUF:Q8_0`
### Other ways to run it
1. Run it as published (BF16, needs ~22.34 GB, from [huggingface.co/speakleash](https://huggingface.co/speakleash/Bielik-11B-v2.1-Instruct/tree/bb3d3ac7da253b1140005a003b3d5d74f0198d26))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='speakleash/Bielik-11B-v2.1-Instruct', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/speakleash](https://huggingface.co/speakleash/Bielik-11B-v2.1-Instruct)
[interactive version](https://whatmodel.sh/model/bielik-11b-v2-1-instruct) · [JSON](https://whatmodel.sh/api?format=json&all=1)