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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BlossomsAI/BloomVN-0.5B-ppo")
model = AutoModelForCausalLM.from_pretrained("BlossomsAI/BloomVN-0.5B-ppo", device_map="auto")
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=256)
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
```

MRAGU

lbourdois commited on May 8, 2025

Commit

53950c2

verified ·

1 Parent(s): 4a8b5a5

Improve language tag (#1)

Browse files

- Improve language tag (6797c83afdd7b372f9e41b936f51cc12ab328d71)

Co-authored-by: Loïck BOURDOIS <lbourdois@users.noreply.huggingface.co>

Files changed (1) hide show

README.md +65 -53

README.md CHANGED Viewed

@@ -1,54 +1,66 @@
----
-license: mit
-language:
-- vi
-datasets:
-- 5CD-AI/Vietnamese-cosmos-qa-gg-translated
-base_model:
-- Qwen/Qwen2.5-0.5B
-library_name: transformers
-tags:
-- text-generation-inference
----
-<div align="center">
-<img src="https://github.com/bloomifycafe/blossomsAI/blob/main/assets/logo.png?raw=true" alt="Logo"/>
-</div>
-</br>
-<div align="center">
-# 🌟 BloomVN-0.5B-ppo
-</div>
-### 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](https://github.com/volcengine/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](https://github.com/BlossomAI)
----
-<div align="center">
-<sub>Star ⭐️ this repo if you find it valuable!</sub>
 </div>

+---
+license: mit
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+datasets:
+- 5CD-AI/Vietnamese-cosmos-qa-gg-translated
+base_model:
+- Qwen/Qwen2.5-0.5B
+library_name: transformers
+tags:
+- text-generation-inference
+---
+<div align="center">
+<img src="https://github.com/bloomifycafe/blossomsAI/blob/main/assets/logo.png?raw=true" alt="Logo"/>
+</div>
+</br>
+<div align="center">
+# 🌟 BloomVN-0.5B-ppo
+</div>
+### 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](https://github.com/volcengine/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](https://github.com/BlossomAI)
+---
+<div align="center">
+<sub>Star ⭐️ this repo if you find it valuable!</sub>
 </div>