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)

# 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")

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 Desktop
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

Atomic Chat

csmpt7b / warnings.py

mfajcik

code upload

27ccd05 verified over 2 years ago

Raw

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894 Bytes

	class VersionedDeprecationWarning(DeprecationWarning):
	"""A custom deprecation warning class that includes version information.

	Attributes:
	message (str): The deprecation message describing why the feature is deprecated.
	remove_version (str): The version in which the feature will be removed.

	Example:
	>>> def deprecated_function():
	... warnings.warn(
	... VersionedDeprecationWarning(
	... "Function XYZ is deprecated.",
	... after_version="2.0.0"
	... )
	... )
	...
	>>> deprecated_function()
	DeprecationWarning: Function XYZ is deprecated. It will be removed in version 2.0.0.
	"""

	def __init__(self, message: str, remove_version: str) -> None:
	super().__init__(message + f' It will be removed in version {remove_version}.')