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
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f29d474 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | import os
import requests
base_url = os.getenv("OPENAI_BASE_URL", "http://localhost:8000/v1")
api_key = os.getenv("OPENAI_API_KEY", "dummy")
def chat_stream():
url = f"{base_url}/chat/completions"
payload = {
"model": "mk-llm",
"messages": [
{"role": "system", "content": "Ти си помошник кој зборува на македонски."},
{"role": "user", "content": "Која е историјата на Охрид?"},
],
"stream": True,
}
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
with requests.post(url, headers=headers, json=payload, stream=True) as r:
for line in r.iter_lines():
if not line:
continue
if line.startswith(b"data: "):
chunk = line[len(b"data: "):]
if chunk == b"[DONE]":
break
print(chunk.decode("utf-8"))
if __name__ == "__main__":
chat_stream()
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