Instructions to use ainowmk/MK-LLM-Mistral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

MK-LLM-Mistral / examples /data_loader.py

ainow-mk

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	from datasets import Dataset, load_dataset
	import pandas as pd
	import os

	def load_mk_dataset():
	"""Load Macedonian dataset for training.

	Prefers the consolidated file produced by data/process_all_data.py:
	data/cleaned/mk_combined_data.txt
	Falls back to per-source files if available.
	"""
	combined_path = 'data/cleaned/mk_combined_data.txt'
	if os.path.exists(combined_path):
	with open(combined_path, 'r', encoding='utf-8') as f:
	texts = [t for t in f.read().split('\n\n') if t.strip()]
	return Dataset.from_dict({'text': texts})

	data_sources = {
	'wikipedia': 'data/cleaned/mk_wiki.txt',
	'news': 'data/cleaned/mk_news.txt',
	'books': 'data/cleaned/mk_books.txt',
	'web': 'data/cleaned/mk_web.txt'
	}
	texts = []
	for _, path in data_sources.items():
	if os.path.exists(path):
	with open(path, 'r', encoding='utf-8') as f:
	texts.extend([line.strip() for line in f if line.strip()])
	return Dataset.from_dict({'text': texts})