Instructions to use MictoNode/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bipedal_exotic_pelican with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MictoNode/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bipedal_exotic_pelican with Transformers:

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

pipe = pipeline("text-generation", model="MictoNode/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bipedal_exotic_pelican")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("MictoNode/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bipedal_exotic_pelican")
model = AutoModelForMultimodalLM.from_pretrained("MictoNode/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bipedal_exotic_pelican")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MictoNode/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bipedal_exotic_pelican with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MictoNode/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bipedal_exotic_pelican"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MictoNode/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bipedal_exotic_pelican",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MictoNode/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bipedal_exotic_pelican

SGLang

How to use MictoNode/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bipedal_exotic_pelican 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 "MictoNode/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bipedal_exotic_pelican" \
    --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": "MictoNode/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bipedal_exotic_pelican",
		"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 "MictoNode/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bipedal_exotic_pelican" \
        --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": "MictoNode/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bipedal_exotic_pelican",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use MictoNode/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bipedal_exotic_pelican 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 MictoNode/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bipedal_exotic_pelican 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 MictoNode/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bipedal_exotic_pelican to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MictoNode/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bipedal_exotic_pelican to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="MictoNode/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bipedal_exotic_pelican",
    max_seq_length=2048,
)

Docker Model Runner
How to use MictoNode/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bipedal_exotic_pelican with Docker Model Runner:
```
docker model run hf.co/MictoNode/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bipedal_exotic_pelican
```

Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bipedal_exotic_pelican

This model is a fine-tuned version of Gensyn/Qwen2.5-0.5B-Instruct. It has been trained using TRL.

Quick start

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="MictoNode/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bipedal_exotic_pelican", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with GRPO, a method introduced in DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Framework versions

TRL: 0.15.2
Transformers: 4.51.3
Pytorch: 2.6.0
Datasets: 3.5.1
Tokenizers: 0.21.1

Citations

Cite GRPO as:

@article{zhihong2024deepseekmath,
    title        = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
    author       = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
    year         = 2024,
    eprint       = {arXiv:2402.03300},
}

Cite TRL as:

@misc{vonwerra2022trl,
    title        = {{TRL: Transformer Reinforcement Learning}},
    author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
    year         = 2020,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{https://github.com/huggingface/trl}}
}