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
| import os | |
| import json | |
| from langdetect import detect | |
| from collections import defaultdict | |
| def test_collected_data(): | |
| print("Testing collected Macedonian data...") | |
| # Paths | |
| raw_data = os.path.join("data", "raw", "mk_web_data.json") | |
| cleaned_data = os.path.join("data", "cleaned", "mk_combined_data.txt") | |
| # Test raw data | |
| if os.path.exists(raw_data): | |
| with open(raw_data, 'r', encoding='utf-8') as f: | |
| web_data = json.load(f) | |
| # Analyze sources | |
| sources = defaultdict(int) | |
| categories = defaultdict(int) | |
| total_chars = 0 | |
| for item in web_data: | |
| sources[item['source']] += 1 | |
| categories[item['category']] += 1 | |
| total_chars += len(item['text']) | |
| print("\n๐ Raw Data Statistics:") | |
| print(f"Total entries: {len(web_data)}") | |
| print(f"Total characters: {total_chars:,}") | |
| print(f"Average entry length: {total_chars/len(web_data):,.0f} characters") | |
| print("\n๐ Categories:") | |
| for cat, count in categories.items(): | |
| print(f"- {cat}: {count} entries") | |
| # Test cleaned data | |
| if os.path.exists(cleaned_data): | |
| with open(cleaned_data, 'r', encoding='utf-8') as f: | |
| cleaned_texts = f.read().split('\n\n') | |
| mk_count = 0 | |
| total_len = 0 | |
| print("\n๐งน Cleaned Data Statistics:") | |
| print(f"Total entries: {len(cleaned_texts)}") | |
| # Test random samples | |
| print("\n๐ Testing random samples:") | |
| import random | |
| for i, text in enumerate(random.sample(cleaned_texts, min(5, len(cleaned_texts)))): | |
| try: | |
| is_mk = detect(text) == 'mk' | |
| mk_count += 1 if is_mk else 0 | |
| total_len += len(text) | |
| print(f"\nSample {i+1} ({len(text)} chars):") | |
| print(f"First 100 chars: {text[:100]}...") | |
| print(f"Language detected: {'Macedonian โ ' if is_mk else 'Other โ'}") | |
| except: | |
| continue | |
| print(f"\nAverage text length: {total_len/len(cleaned_texts):,.0f} characters") | |
| if __name__ == "__main__": | |
| test_collected_data() |