Instructions to use bcckfdn/cevher-test-4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use bcckfdn/cevher-test-4-GGUF 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 bcckfdn/cevher-test-4-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bcckfdn/cevher-test-4-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf bcckfdn/cevher-test-4-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bcckfdn/cevher-test-4-GGUF:Q4_K_M

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 bcckfdn/cevher-test-4-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bcckfdn/cevher-test-4-GGUF:Q4_K_M

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 bcckfdn/cevher-test-4-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bcckfdn/cevher-test-4-GGUF:Q4_K_M

Use Docker

docker model run hf.co/bcckfdn/cevher-test-4-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use bcckfdn/cevher-test-4-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bcckfdn/cevher-test-4-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bcckfdn/cevher-test-4-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bcckfdn/cevher-test-4-GGUF:Q4_K_M

Ollama
How to use bcckfdn/cevher-test-4-GGUF with Ollama:
```
ollama run hf.co/bcckfdn/cevher-test-4-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use bcckfdn/cevher-test-4-GGUF with Docker Model Runner:
```
docker model run hf.co/bcckfdn/cevher-test-4-GGUF:Q4_K_M
```

Lemonade

How to use bcckfdn/cevher-test-4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bcckfdn/cevher-test-4-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.cevher-test-4-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

cevher-406m — GGUF

SmolLM2 (Llama) mimarisiyle sıfırdan eğitilmiş modelin GGUF sürümleri. Eğitim token sayısı: 3.932 B · hidden 1024 · 34 katman.

Dosya	Boyut
`cevher-406m-BF16.gguf`	778 MB
`cevher-406m-Q8_0.gguf`	414 MB
`cevher-406m-Q5_K_M.gguf`	281 MB
`cevher-406m-Q4_K_M.gguf`	245 MB

llama.cpp

llama-cli -m cevher-406m-Q4_K_M.gguf -p "Merhaba" -cnv

Ollama

ollama create cevher-406m -f Modelfile
ollama run cevher-406m

LM Studio

Dosyayı ~/.cache/lm-studio/models/bcckfdn/cevher-test-4-GGUF/ altına koyun ya da uygulama içinden arayın.

Downloads last month: 107

GGUF

Model size

0.4B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

8-bit

16-bit

Model tree for bcckfdn/cevher-test-4-GGUF

Base model

bcckfdn/cevher-test-4

Quantized

(1)

this model