Instructions to use hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-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 hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-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 hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-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 hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-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 hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-GGUF:Q4_K_M

SGLang

How to use hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-GGUF 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 "hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-GGUF" \
    --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": "hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-GGUF",
		"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 "hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-GGUF" \
        --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": "hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-GGUF with Ollama:
```
ollama run hf.co/hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-GGUF:Q4_K_M
```

Lemonade

How to use hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Lodos-24B-Instruct-2510-Q4_K_M-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Lodos-24B-Instruct-2510-Q4_K_M-GGUF / README.md

hasanbasbunar

Upload README.md with huggingface_hub

6cb15d2 verified 11 months ago

preview code

Raw

History Blame Contribute Delete

2.04 kB

metadata

base_model: hasanbasbunar/Lodos-24B-Instruct-2510
library_name: transformers
model_name: Lodos-24B-Instruct-2510
tags:
  - generated_from_trainer
  - unsloth
  - sft
  - trl
  - llama-cpp
  - gguf-my-repo
licence: license
language:
  - tr
pipeline_tag: image-text-to-text

hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-GGUF

This model was converted to GGUF format from hasanbasbunar/Lodos-24B-Instruct-2510 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-GGUF --hf-file lodos-24b-instruct-2510-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-GGUF --hf-file lodos-24b-instruct-2510-q4_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-GGUF --hf-file lodos-24b-instruct-2510-q4_k_m.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo hasanbasbunar/Lodos-24B-Instruct-2510-Q4_K_M-GGUF --hf-file lodos-24b-instruct-2510-q4_k_m.gguf -c 2048