Instructions to use trajkovnikola/MKLLM-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use trajkovnikola/MKLLM-7B with Transformers:

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

pipe = pipeline("text-generation", model="trajkovnikola/MKLLM-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("trajkovnikola/MKLLM-7B")
model = AutoModelForCausalLM.from_pretrained("trajkovnikola/MKLLM-7B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use trajkovnikola/MKLLM-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "trajkovnikola/MKLLM-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "trajkovnikola/MKLLM-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/trajkovnikola/MKLLM-7B

SGLang

How to use trajkovnikola/MKLLM-7B 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 "trajkovnikola/MKLLM-7B" \
    --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": "trajkovnikola/MKLLM-7B",
		"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 "trajkovnikola/MKLLM-7B" \
        --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": "trajkovnikola/MKLLM-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use trajkovnikola/MKLLM-7B with Docker Model Runner:
```
docker model run hf.co/trajkovnikola/MKLLM-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

MKLLM-7B

MKLLM-7B is an open-source Large Language Model for the Macedonian language. The model is built on top of the amazing Mistral-7B-v0.1 model by continued pretraining on a mix of Macedonian and English text. A corpus of around 300M tokens, repeated in 2 epochs, was used for the training and even though this might be considered small compared to other similar projects, the resulting model is very capable in understanding and processing the Macedonian language.

We have built two instruction models on top of the base model which showcase the potential of the model.

MKLLM-7B-Instruct: An instruction-tuned that performs better than leading models from the same size:

MKLLM-7B-Translate: An LLM as a translator implementation that has quite an impressive performance:

Notes

MKLLM-7B is a base model and is not intended for deployment without fine-tuning. The model has no moderation mechanisms.
MKLLM-7B can hallucinate and produce factually incorrect output. This is especially pronounced when discussing Macedonian topics due to the smaller training dataset.

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Safetensors

Model size

7B params

Tensor type

BF16

Model tree for trajkovnikola/MKLLM-7B

Quantizations

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