Instructions to use BlossomsAI/BloomVN-0.5B-ppo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BlossomsAI/BloomVN-0.5B-ppo with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("BlossomsAI/BloomVN-0.5B-ppo")
model = AutoModelForMultimodalLM.from_pretrained("BlossomsAI/BloomVN-0.5B-ppo")
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 BlossomsAI/BloomVN-0.5B-ppo with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/BlossomsAI/BloomVN-0.5B-ppo

SGLang

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

Docker Model Runner
How to use BlossomsAI/BloomVN-0.5B-ppo with Docker Model Runner:
```
docker model run hf.co/BlossomsAI/BloomVN-0.5B-ppo
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🌟 BloomVN-0.5B-ppo

A fine-tuned multilingual model for Vietnamese language

📋 Overview

This model serves as a small-scale experiment (0.5B parameters) testing the Reinforcement Learning capabilities of veRL framework. The implementation uses PPO (Proximal Policy Optimization) method on a limited training dataset to evaluate veRL's performance and training behavior.

🔧 Method

The experimentation process was conducted using veRL, focusing on:

Implementation of PPO algorithm with a 0.5B parameter model
Running training experiments on a small dataset
Testing veRL's framework capabilities in handling RL tasks
Evaluating training efficiency and model behavior

This lightweight approach allowed us to assess veRL's performance in a controlled, small-scale environment.

📊 VLMU Benchmark

EVALUATION DATE	STEM 🔬	SOCIAL SCIENCE 🌍	HUMANITIES 📚	OTHERS 🎯	AVG ⭐
07/02/2025	23.18	32.84	32.71	33.67	29.43

🤝 Contributors

Developed with ❤️ by BlossomAI

_{Star ⭐️ this repo if you find it valuable!}

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Safetensors

Model size

0.5B params

Tensor type

F32

Model tree for BlossomsAI/BloomVN-0.5B-ppo

Base model

Qwen/Qwen2.5-0.5B

Finetuned

(622)

this model

Adapters

24 models

Quantizations

2 models

BlossomsAI
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BloomVN-0.5B-ppo