Instructions to use InosLihka/rhythm-env-meta-trained-iter5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use InosLihka/rhythm-env-meta-trained-iter5 with Transformers:

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

pipe = pipeline("text-generation", model="InosLihka/rhythm-env-meta-trained-iter5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("InosLihka/rhythm-env-meta-trained-iter5")
model = AutoModelForCausalLM.from_pretrained("InosLihka/rhythm-env-meta-trained-iter5")
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

vLLM

How to use InosLihka/rhythm-env-meta-trained-iter5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "InosLihka/rhythm-env-meta-trained-iter5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "InosLihka/rhythm-env-meta-trained-iter5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/InosLihka/rhythm-env-meta-trained-iter5

SGLang

How to use InosLihka/rhythm-env-meta-trained-iter5 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 "InosLihka/rhythm-env-meta-trained-iter5" \
    --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": "InosLihka/rhythm-env-meta-trained-iter5",
		"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 "InosLihka/rhythm-env-meta-trained-iter5" \
        --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": "InosLihka/rhythm-env-meta-trained-iter5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use InosLihka/rhythm-env-meta-trained-iter5 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 InosLihka/rhythm-env-meta-trained-iter5 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 InosLihka/rhythm-env-meta-trained-iter5 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for InosLihka/rhythm-env-meta-trained-iter5 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="InosLihka/rhythm-env-meta-trained-iter5",
    max_seq_length=2048,
)

Docker Model Runner
How to use InosLihka/rhythm-env-meta-trained-iter5 with Docker Model Runner:
```
docker model run hf.co/InosLihka/rhythm-env-meta-trained-iter5
```

rhythm-env-meta-trained-iter5 / README.md

InosLihka

Trained 500-step GRPO meta-RL agent

7eff898 verified 13 days ago

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	---
	base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
	library_name: transformers
	model_name: rhythmenv_meta_trained
	tags:
	- generated_from_trainer
	- unsloth
	- trl
	- grpo
	- hf_jobs
	licence: license
	---

	# Model Card for rhythmenv_meta_trained

	This model is a fine-tuned version of [unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit](https://huggingface.co/unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit).
	It has been trained using [TRL](https://github.com/huggingface/trl).

	## Quick start

	```python
	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="None", 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](https://huggingface.co/papers/2402.03300).

	### Framework versions

	- TRL: 0.22.2
	- Transformers: 4.56.2
	- Pytorch: 2.10.0
	- Datasets: 4.3.0
	- Tokenizers: 0.22.2

	## Citations

	Cite GRPO as:

	```bibtex
	@article{shao2024deepseekmath,
	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:

	```bibtex
	@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 Gallou{\'e}dec},
	year = 2020,
	journal = {GitHub repository},
	publisher = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
	}
	```