Instructions to use alphadl/ppo-gsm8k-0.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alphadl/ppo-gsm8k-0.5b with Transformers:

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

pipe = pipeline("text-generation", model="alphadl/ppo-gsm8k-0.5b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("alphadl/ppo-gsm8k-0.5b")
model = AutoModelForCausalLM.from_pretrained("alphadl/ppo-gsm8k-0.5b")
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 alphadl/ppo-gsm8k-0.5b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alphadl/ppo-gsm8k-0.5b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alphadl/ppo-gsm8k-0.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/alphadl/ppo-gsm8k-0.5b

SGLang

How to use alphadl/ppo-gsm8k-0.5b 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 "alphadl/ppo-gsm8k-0.5b" \
    --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": "alphadl/ppo-gsm8k-0.5b",
		"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 "alphadl/ppo-gsm8k-0.5b" \
        --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": "alphadl/ppo-gsm8k-0.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use alphadl/ppo-gsm8k-0.5b with Docker Model Runner:
```
docker model run hf.co/alphadl/ppo-gsm8k-0.5b
```

ppo-gsm8k-0.5b / README.md

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	---
	base_model: Qwen/Qwen2.5-0.5B-Instruct
	library_name: transformers
	tags:
	- qwen2
	- math
	- reasoning
	- ppo
	- rlhf
	- gsm8k
	- merged-model
	- verl
	language:
	- en
	pipeline_tag: text-generation
	---

	# Qwen2.5-0.5B-Instruct-GSM8K-PPO-Merged
	(perhaps) state-of-the-art math models on GSM8k with less than 0.5 billion parameters

	## 📊 Model Overview

	This model is a merged version of multiple high-performing checkpoints derived from fine-tuning Qwen2.5-0.5B-Instruct using PPO (Proximal Policy Optimization) on the GSM8K mathematical reasoning dataset.

	### 🎯 Key Features
	- Base Model: Qwen/Qwen2.5-0.5B-Instruct (494M parameters)
	- Training Algorithm: PPO via [VERL](https://github.com/volcengine/verl) framework
	- Specialization: Mathematical reasoning and problem-solving
	- Model Merging: Averaged from 3 best-performing checkpoints using [mergekit](https://github.com/cg123/mergekit)

	## 📈 Performance

	\| Dataset \| Score \| Improvement \|
	\|---------\|-------\|-------------\|
	\| GSM8K \| 58.91% \| +9.31% over qwen2.5-0.5b-instruct model \|


	> This represents a significant improvement in mathematical reasoning capabilities for a 0.5B parameter model.

	## 🔧 Usage

	### Quick Start

	```python
	from transformers import AutoTokenizer, AutoModelForCausalLM
	import torch

	# Load model and tokenizer
	model_name = "alphadl/ppo-gsm8k-0.5b"
	tokenizer = AutoTokenizer.from_pretrained(model_name)
	model = AutoModelForCausalLM.from_pretrained(
	model_name,
	torch_dtype=torch.bfloat16,
	device_map="auto"
	)

	# Example: Mathematical reasoning
	prompt = """Solve this step by step:
	Janet's ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder at the farmers' market daily for $2 per fresh duck egg. How much in dollars does she make every day at the farmers' market?

	Let's think step by step and output the final answer after "####"."""

	inputs = tokenizer(prompt, return_tensors="pt")
	with torch.no_grad():
	outputs = model.generate(
	**inputs,
	max_new_tokens=256,
	temperature=0.0,
	do_sample=False,
	pad_token_id=tokenizer.eos_token_id
	)

	response = tokenizer.decode(outputs[0], skip_special_tokens=True)
	print(response)
	```

	### Expected Output Format
	The model is trained to provide step-by-step mathematical reasoning followed by the final answer in the format:
	```
	#### [numerical_answer]
	```

	## 🛠️ Training Details

	### Training Framework
	- Framework: [VERL (Volcano Engine Reinforcement Learning)](https://github.com/volcengine/verl)
	- Algorithm: PPO (Proximal Policy Optimization)
	- Data Source: GSM8K mathematical reasoning dataset

	### Model Merging Strategy
	This model was created by merging 6 high-performing checkpoints using linear interpolation:

	\| Checkpoint \| GSM8K Score \| Weight \|
	\|------------\|-------------\|---------\|
	\| global_step_5000 \| 58.3% \| 33% \|
	\| global_step_6000 \| 58.7% \| 33% \|
	\| global_step_7500 \| 58.9% \| 34% \|

	Result: The merged model achieved 58.91%, surpassing individual checkpoints!

	### Training Configuration
	- Base Model: Qwen/Qwen2.5-0.5B-Instruct
	- Training Steps: 7,500+ steps
	- Validation Frequency: Every 1,000 steps
	- Optimization: AdamW with learning rate scheduling

	## 🎯 Use Cases

	This model excels at:
	- Mathematical Problem Solving: Arithmetic, algebra, basic geometry
	- Step-by-Step Reasoning: Breaking down complex problems
	- Educational Applications: Math tutoring and explanation
	- Computational Tasks: Basic calculations with reasoning

	## ⚠️ Limitations

	- Model Size: As a 0.5B parameter model, it may struggle with very complex mathematical concepts
	- Domain Specificity: Optimized for GSM8K-style problems; may not perform as well on other domains
	- Context Length: Limited by the base model's context window (32K tokens)

	## 📄 License

	This model inherits the license from the base Qwen2.5-0.5B-Instruct model. Please refer to the [original model card](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) for licensing details.

	## 🙏 Acknowledgments

	- Base Model: [Qwen Team](https://github.com/QwenLM/Qwen2.5) for Qwen2.5-0.5B-Instruct
	- Training Framework: [VERL Team](https://github.com/volcengine/verl) for the PPO implementation
	- Model Merging: [mergekit](https://github.com/cg123/mergekit) for the averaging capabilities
	- Dataset: [GSM8K](https://github.com/openai/grade-school-math) for mathematical reasoning data

	## 📬 Contact

	For questions or issues, please open an issue in the repository or contact the model author.

	---

	This model was trained using VERL framework and merged using mergekit for optimal mathematical reasoning performance.