Instructions to use BUT-FIT/csmpt7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BUT-FIT/csmpt7b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="BUT-FIT/csmpt7b", trust_remote_code=True, device_map="auto")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BUT-FIT/csmpt7b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("BUT-FIT/csmpt7b", trust_remote_code=True, device_map="auto")

llama-cpp-python

How to use BUT-FIT/csmpt7b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="BUT-FIT/csmpt7b",
	filename="BUT-FIT_csmpt7b-6.7B-BF16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use BUT-FIT/csmpt7b with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf BUT-FIT/csmpt7b:BF16
# Run inference directly in the terminal:
llama cli -hf BUT-FIT/csmpt7b:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf BUT-FIT/csmpt7b:BF16
# Run inference directly in the terminal:
llama cli -hf BUT-FIT/csmpt7b:BF16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf BUT-FIT/csmpt7b:BF16
# Run inference directly in the terminal:
./llama-cli -hf BUT-FIT/csmpt7b:BF16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf BUT-FIT/csmpt7b:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf BUT-FIT/csmpt7b:BF16

Use Docker

docker model run hf.co/BUT-FIT/csmpt7b:BF16

LM Studio
Jan

vLLM

How to use BUT-FIT/csmpt7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BUT-FIT/csmpt7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BUT-FIT/csmpt7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/BUT-FIT/csmpt7b:BF16

SGLang

How to use BUT-FIT/csmpt7b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "BUT-FIT/csmpt7b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BUT-FIT/csmpt7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "BUT-FIT/csmpt7b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BUT-FIT/csmpt7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use BUT-FIT/csmpt7b with Ollama:
```
ollama run hf.co/BUT-FIT/csmpt7b:BF16
```

Unsloth Studio

How to use BUT-FIT/csmpt7b with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for BUT-FIT/csmpt7b to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for BUT-FIT/csmpt7b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for BUT-FIT/csmpt7b to start chatting

Atomic Chat new
Docker Model Runner
How to use BUT-FIT/csmpt7b with Docker Model Runner:
```
docker model run hf.co/BUT-FIT/csmpt7b:BF16
```

Lemonade

How to use BUT-FIT/csmpt7b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull BUT-FIT/csmpt7b:BF16

Run and chat with the model

lemonade run user.csmpt7b-BF16

List all available models

lemonade list

mfajcik commited on May 21, 2025

Commit

dea96b2

verified ·

1 Parent(s): 401fc64

Update README.md

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@@ -5,6 +5,9 @@ datasets:
 - BUT-FIT/adult_content_classifier_dataset
 language:
 - cs
 ---
 # Introduction
 CSMPT7b is a large Czech language model continously pretrained on 272b training tokens from English [MPT7b](https://huggingface.co/mosaicml/mpt-7b) model. Model was pretrained on ~67b token [Large Czech Collection](https://huggingface.co/datasets/BUT-FIT/BUT-LCC) using Czech tokenizer, obtained using our vocabulary swap method (see below).
@@ -20,7 +23,6 @@ Training was done on [Karolina](https://www.it4i.cz/en) cluster.
 - 01/10/2024 We released [BenCzechMark](https://huggingface.co/spaces/CZLC/BenCzechMark), the first Czech evaluation suite for fair open-weights model comparison.
 - 18/04/2024 We released all our training checkpoints (in MosaicML format & packed using ZPAQ) at [czechllm.fit.vutbr.cz/csmpt7b/checkpoints/](https://czechllm.fit.vutbr.cz/csmpt7b/checkpoints/)
 - 06/05/2024 We released small manually annotated [dataset of adult content](https://huggingface.co/datasets/BUT-FIT/adult_content_classifier_dataset). We used classifier trained on this dataset for filtering our corpus.
--
 # Evaluation
 Dev eval at CS-HellaSwag  (automatically translated HellaSwag benchmark).
 | Model | CS-HellaSwag Accuracy |

 - BUT-FIT/adult_content_classifier_dataset
 language:
 - cs
+tags:
+- llama-cpp
+- gguf-my-repo
 ---
 # Introduction
 CSMPT7b is a large Czech language model continously pretrained on 272b training tokens from English [MPT7b](https://huggingface.co/mosaicml/mpt-7b) model. Model was pretrained on ~67b token [Large Czech Collection](https://huggingface.co/datasets/BUT-FIT/BUT-LCC) using Czech tokenizer, obtained using our vocabulary swap method (see below).
 - 01/10/2024 We released [BenCzechMark](https://huggingface.co/spaces/CZLC/BenCzechMark), the first Czech evaluation suite for fair open-weights model comparison.
 - 18/04/2024 We released all our training checkpoints (in MosaicML format & packed using ZPAQ) at [czechllm.fit.vutbr.cz/csmpt7b/checkpoints/](https://czechllm.fit.vutbr.cz/csmpt7b/checkpoints/)
 - 06/05/2024 We released small manually annotated [dataset of adult content](https://huggingface.co/datasets/BUT-FIT/adult_content_classifier_dataset). We used classifier trained on this dataset for filtering our corpus.
 # Evaluation
 Dev eval at CS-HellaSwag  (automatically translated HellaSwag benchmark).
 | Model | CS-HellaSwag Accuracy |