Instructions to use AnonymousCodeX/pprl-1-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AnonymousCodeX/pprl-1-small with Transformers:

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

pipe = pipeline("text-generation", model="AnonymousCodeX/pprl-1-small")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AnonymousCodeX/pprl-1-small")
model = AutoModelForCausalLM.from_pretrained("AnonymousCodeX/pprl-1-small", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AnonymousCodeX/pprl-1-small with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AnonymousCodeX/pprl-1-small"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AnonymousCodeX/pprl-1-small",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AnonymousCodeX/pprl-1-small

SGLang

How to use AnonymousCodeX/pprl-1-small 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 "AnonymousCodeX/pprl-1-small" \
    --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": "AnonymousCodeX/pprl-1-small",
		"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 "AnonymousCodeX/pprl-1-small" \
        --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": "AnonymousCodeX/pprl-1-small",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use AnonymousCodeX/pprl-1-small 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 AnonymousCodeX/pprl-1-small 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 AnonymousCodeX/pprl-1-small to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AnonymousCodeX/pprl-1-small to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="AnonymousCodeX/pprl-1-small",
    max_seq_length=2048,
)

Docker Model Runner
How to use AnonymousCodeX/pprl-1-small with Docker Model Runner:
```
docker model run hf.co/AnonymousCodeX/pprl-1-small
```

pprl-1-small / README.md

AnonymousCodeX

Update README.md

4ad5c37 verified 11 months ago

preview code

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	---
	base_model:
	- Qwen/Qwen3-4B-Thinking-2507
	tags:
	- text-generation-inference
	- transformers
	- unsloth
	- qwen3
	license: apache-2.0
	language:
	- en
	---
	PPRL-1-Small is an advanced language model specifically optimized for high-quality writing generation. It is finetuned from Qwen3-4B-thinking-2507 using a Online BNPO (GRPO variant) training methodology. This approach significantly enhances the model's ability to perform deep thinking, resulting in outputs with superior creativity, logical coherence, and narrative depth.

	Training Procedure
	Preprocessing: Used deepseek r1 0528 and deepseek v3.1 generated 10k samples of creative writing. Then sft.

	SFT Fine-tuning 2: Used our own private dataset and done 1252 steps of supervised finetuning.

	RL Fine-tuning: Online BNPO alignment using unsloth with a private critic model generate critic data,then use dsv3.1 as reward model.

	Hardware: single A800 80GB GPU
	Training Time: Approximately 72 GPU Hours


	Open-Source Contribution: The qwen3_4 Dataset
	We have open-sourced a portion of the dataset used for the BNPO training phase as qwen3_4. We believe open collaboration is key to progress and invite the community to contribute to and expand this dataset to help advance the state of AI-assisted writing.
	You can commit to the dataset to support our work.

	Uses
	The model is intended for:

	Creative Writing: Generating stories, poetry, scripts, and other narrative content.
	Long-Form Content Creation: Writing essays, articles, reports, and blog posts with strong logical flow.
	Content Enhancement & Rewriting: Improving the creativity and coherence of existing text.

	IMPORTANT
	For any commercial use, you MUST report to me first. Otherwise, it will be considered an illegal action.

	How to Get Started with the Model
	Normal transformers inference framework is all available. Use it as a normal Qwen3 2507 thinking model.

	Future Work
	This is just the beginning. We are continuously working on training larger and more capable models. Stay tuned for more updates!
	If you are interested in supporting my work or hiring me for a project, please feel free to contact me via email. I will be sharing my contact details here shortly.

	# Uploaded finetuned model

	- Developed by: AnonymousCodeX
	- License: apache-2.0
	- Finetuned from model : Qwen/qwen3-4b-thinking-2507

	This qwen3 model was trained 2x faster with [Unsloth] and Huggingface's TRL library.