Instructions to use epsill0n/genesis-4B-DEMO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use epsill0n/genesis-4B-DEMO with Transformers:

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

pipe = pipeline("text-generation", model="epsill0n/genesis-4B-DEMO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("epsill0n/genesis-4B-DEMO")
model = AutoModelForCausalLM.from_pretrained("epsill0n/genesis-4B-DEMO", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use epsill0n/genesis-4B-DEMO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "epsill0n/genesis-4B-DEMO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "epsill0n/genesis-4B-DEMO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/epsill0n/genesis-4B-DEMO

SGLang

How to use epsill0n/genesis-4B-DEMO 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 "epsill0n/genesis-4B-DEMO" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "epsill0n/genesis-4B-DEMO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "epsill0n/genesis-4B-DEMO" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "epsill0n/genesis-4B-DEMO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use epsill0n/genesis-4B-DEMO with Docker Model Runner:
```
docker model run hf.co/epsill0n/genesis-4B-DEMO
```

Genesis (genesis-4B-DEMO)

Genesis is a a fine-tune variant of unsloth/Qwen3.5-4B, fine-tuned on a curated dataset of 1,171 Macedonian mathematical competition problem-solution pairs. This release marks the beginning of a bigger project, and a new series of language models, enhancing mathematical reasoning capabilities in under-resourced languages.

Model Details

Base Model: unsloth/Qwen3.5-4B
Adapter Type: LoRA (Rank r=16, Alpha α=32, Dropout 0.0)
Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Precision: bf16
Max Sequence Length: 1024
Training Epochs: 10 (peak performance observed at epoch 3)
Hardware: Single NVIDIA RTX 3060 12GB

Training Data

The model was fine-tuned on a manually verified dataset of Macedonian mathematical competition problems. The pipeline converts public PDFs into structured LaTeX, extracts problem-solution pairs, and applies rigorous manual verification. The final dataset contains 1,171 entries covering algebra, number theory, combinatorics, and arithmetic reasoning. Geometry problems requiring figure reconstruction were excluded due to pipeline limitations.

Dataset Availability: The training dataset is not publicly released. Only the model weights and training/evaluation code are provided.

Evaluation

Benchmark	Base Qwen3.5-4B	Genesis (Epoch 3)	Genesis (Epoch 10)
GSM8K_mk (748 problems)	9.90%	57.75%	44.92%
macedonian-llm-eval (avg)	0.47	0.47	0.47

GSM8K_mk: Macedonian translation of GSM8K. Pass@1 with greedy decoding and regex-based answer extraction.
macedonian-llm-eval: Seven standard benchmarks (arc_challenge, arc_easy, boolq, hellaswag, openbookqa, piqa, winogrande). Zero-shot, greedy decoding.
Key Finding: Performance peaks at epoch 3 and degrades monotonically with extended training, despite continued reduction in training loss. General Macedonian capabilities are preserved.

Pass@1 accuracy on the 748-problem Macedonian GSM8K test set across Genesis checkpoints and baseline models. Performance peaks at epoch 3 and declines monotonically.

Performance comparison across the seven macedonian-llm-eval benchmarks. Genesis (E3) maintains parity with the base model, confirming preservation of general Macedonian capabilities.

Usage

Prerequisites

pip install unsloth transformers torch datasets peft accelerate

Loading the Adapter

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "a-nikoloski/genesis-4b-demo"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, torch_dtype=torch.bfloat16, device_map="auto"
)

messages = [{"role": "user", "content": "Пресметај го збирот на броевите од 1 до 10."}]
inputs = tok.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True,
    return_tensors="pt", return_dict=True,
).to(model.device)

with torch.no_grad():
    out = model.generate(
        **inputs, max_new_tokens=512, do_sample=False,
        repetition_penalty=1.1, pad_token_id=tok.pad_token_id or tok.eos_token_id,
    )

print(tok.decode(out[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

Citation

If you use this model or the associated training/evaluation code, please cite:

@misc{genesis2026,
  author = {Aleksandar Nikoloski},
  title = {Genesis: Towards Macedonian Mathematical Language Models},
  year = {2026},
  url = {https://huggingface.co/epsill0n/genesis-4B-DEMO}
}