Text Generation
Transformers
PyTorch
Safetensors
PEFT
Macedonian
opt
macedonian
cyrillic
mistral
qlora
text-generation-inference
Instructions to use ainowmk/MK-LLM-Mistral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ainowmk/MK-LLM-Mistral with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ainowmk/MK-LLM-Mistral")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ainowmk/MK-LLM-Mistral") model = AutoModelForCausalLM.from_pretrained("ainowmk/MK-LLM-Mistral", device_map="auto") - PEFT
How to use ainowmk/MK-LLM-Mistral with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ainowmk/MK-LLM-Mistral with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ainowmk/MK-LLM-Mistral" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ainowmk/MK-LLM-Mistral", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ainowmk/MK-LLM-Mistral
- SGLang
How to use ainowmk/MK-LLM-Mistral 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 "ainowmk/MK-LLM-Mistral" \ --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": "ainowmk/MK-LLM-Mistral", "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 "ainowmk/MK-LLM-Mistral" \ --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": "ainowmk/MK-LLM-Mistral", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ainowmk/MK-LLM-Mistral with Docker Model Runner:
docker model run hf.co/ainowmk/MK-LLM-Mistral
File size: 625 Bytes
f29d474 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | from data_loader import load_mk_dataset
def test_dataset():
print("Testing Macedonian dataset loader...")
# Load the dataset
dataset = load_mk_dataset()
# Print basic statistics
print(f"\nDataset size: {len(dataset)} examples")
# Show first few examples
print("\nFirst 3 examples:")
for i in range(min(3, len(dataset))):
text = dataset[i]['text']
print(f"\nExample {i+1}:")
print(f"Length: {len(text)} characters")
print(f"Preview: {text[:200]}...") # Show first 200 characters
if __name__ == "__main__":
test_dataset() |