Instructions to use zumberisclown/Vesna-R1-1.5B-Reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zumberisclown/Vesna-R1-1.5B-Reasoning with Transformers:

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

pipe = pipeline("text-generation", model="zumberisclown/Vesna-R1-1.5B-Reasoning")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("zumberisclown/Vesna-R1-1.5B-Reasoning", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zumberisclown/Vesna-R1-1.5B-Reasoning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zumberisclown/Vesna-R1-1.5B-Reasoning"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zumberisclown/Vesna-R1-1.5B-Reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zumberisclown/Vesna-R1-1.5B-Reasoning

SGLang

How to use zumberisclown/Vesna-R1-1.5B-Reasoning 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 "zumberisclown/Vesna-R1-1.5B-Reasoning" \
    --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": "zumberisclown/Vesna-R1-1.5B-Reasoning",
		"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 "zumberisclown/Vesna-R1-1.5B-Reasoning" \
        --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": "zumberisclown/Vesna-R1-1.5B-Reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use zumberisclown/Vesna-R1-1.5B-Reasoning with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for zumberisclown/Vesna-R1-1.5B-Reasoning to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for zumberisclown/Vesna-R1-1.5B-Reasoning to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for zumberisclown/Vesna-R1-1.5B-Reasoning to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="zumberisclown/Vesna-R1-1.5B-Reasoning",
    max_seq_length=2048,
)

Docker Model Runner
How to use zumberisclown/Vesna-R1-1.5B-Reasoning with Docker Model Runner:
```
docker model run hf.co/zumberisclown/Vesna-R1-1.5B-Reasoning
```

Vesna-R1-1.5B-Reasoning

Vesna-R1-1.5B-Reasoning is a fine-tuned reasoning-oriented adapter based on unsloth/deepseek-r1-distill-qwen-1.5b-bnb-4bit.

This repository contains a lightweight fine-tuned adapter trained with Unsloth, designed to improve reasoning-style responses, instruction following, and overall conversational coherence while preserving the efficiency of the original 1.5B base model.

Model Details

Model name: Vesna-R1-1.5B-Reasoning
Developed by: zumberisclown
Base model: unsloth/deepseek-r1-distill-qwen-1.5b-bnb-4bit
License: Apache-2.0
Language(s): English
Frameworks: Transformers, TRL, Unsloth

Description

This model is a fine-tuned adapter built on top of a distilled DeepSeek-R1 Qwen 1.5B variant.
It was trained with Unsloth, which enables faster and more memory-efficient fine-tuning.

The goal of this project is to enhance the base model’s performance on:

reasoning-style generations
instruction-following tasks
conversational responses
structured answer formatting

Training

Base model: unsloth/deepseek-r1-distill-qwen-1.5b-bnb-4bit
Training library: Unsloth
Model type: Fine-tuned adapter
Optimization goal: Efficient reasoning-focused instruction tuning

This model was trained using Unsloth, allowing significantly faster fine-tuning compared to standard approaches.

Intended Use

This model is intended for:

general instruction following
lightweight reasoning tasks
experimentation with small reasoning-oriented language models
research and hobbyist workflows

Limitations

As a 1.5B parameter class model, this adapter has important limitations:

it may struggle with complex multi-step reasoning
it is not guaranteed to be reliable for factual or high-stakes tasks
performance may vary significantly outside the training distribution
outputs should be reviewed before use in production or critical settings

Usage

Make sure to load the base model together with the adapter weights from this repository.

Acknowledgements

This project was trained with Unsloth, an excellent library for fast and memory-efficient LLM fine-tuning.

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