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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metadata

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 framework
Specialization: Mathematical reasoning and problem-solving
Model Merging: Averaged from 3 best-performing checkpoints using 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

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)
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 for licensing details.

🙏 Acknowledgments

Base Model: Qwen Team for Qwen2.5-0.5B-Instruct
Training Framework: VERL Team for the PPO implementation
Model Merging: mergekit for the averaging capabilities
Dataset: GSM8K 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.