Instructions to use LorMolf/SPSD-RL-Qwen3-4B-Factory-MHTrue-2Ep-20260608 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LorMolf/SPSD-RL-Qwen3-4B-Factory-MHTrue-2Ep-20260608 with Transformers:

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

pipe = pipeline("text-generation", model="LorMolf/SPSD-RL-Qwen3-4B-Factory-MHTrue-2Ep-20260608")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LorMolf/SPSD-RL-Qwen3-4B-Factory-MHTrue-2Ep-20260608")
model = AutoModelForCausalLM.from_pretrained("LorMolf/SPSD-RL-Qwen3-4B-Factory-MHTrue-2Ep-20260608", 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 LorMolf/SPSD-RL-Qwen3-4B-Factory-MHTrue-2Ep-20260608 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LorMolf/SPSD-RL-Qwen3-4B-Factory-MHTrue-2Ep-20260608"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LorMolf/SPSD-RL-Qwen3-4B-Factory-MHTrue-2Ep-20260608",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LorMolf/SPSD-RL-Qwen3-4B-Factory-MHTrue-2Ep-20260608

SGLang

How to use LorMolf/SPSD-RL-Qwen3-4B-Factory-MHTrue-2Ep-20260608 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 "LorMolf/SPSD-RL-Qwen3-4B-Factory-MHTrue-2Ep-20260608" \
    --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": "LorMolf/SPSD-RL-Qwen3-4B-Factory-MHTrue-2Ep-20260608",
		"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 "LorMolf/SPSD-RL-Qwen3-4B-Factory-MHTrue-2Ep-20260608" \
        --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": "LorMolf/SPSD-RL-Qwen3-4B-Factory-MHTrue-2Ep-20260608",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LorMolf/SPSD-RL-Qwen3-4B-Factory-MHTrue-2Ep-20260608 with Docker Model Runner:
```
docker model run hf.co/LorMolf/SPSD-RL-Qwen3-4B-Factory-MHTrue-2Ep-20260608
```

Qwen3-4B SPSD-RL LLaMA-Factory SFT, mask_history=true, 2 epochs

This model is a full-parameter fine-tune of Qwen/Qwen3-4B-Base on LorMolf/SPSD-RL using the repository LLaMA-Factory SFT pipeline.

This upload is intentionally separate from the earlier LorMolf/SPSD-RL model repo so previous uploaded models are not overwritten.

Training

Base model: Qwen/Qwen3-4B-Base
Dataset: LorMolf/SPSD-RL, data/*.jsonl
Dataset revision: 76e62ee11f0b6b8e9a5511a7044a556f7c0c8e42
Pipeline: src/training_eval/train_sft_factory.py
Template: qwen
Supervision: prompt/completion assistant-turn expansion
LLaMA-Factory masking: train_on_prompt=false, mask_history=true
Sequence length: 16384
Epochs: 2
Per-device train batch size: 1
GPUs: 4
Gradient accumulation steps: 16
Effective train batch size: 64
Learning rate: 2e-5
Warmup ratio: 0.03
Scheduler: linear
Precision: bf16
Packing: true, neat_packing=true

Final training metrics from the local run:

train_loss: 0.06013819321350911
train_runtime: 22:30:13.22
train_steps_per_second: 0.011
Final epoch: 2.0

W&B run: https://wandb.ai/lorenzo-molfetta/olmo-spiral-sft/runs/qr44c5qn

Notes

This run was launched before train-time validation was added to the factory pipeline, so it has no validation metrics. Use the repository generation evaluation pipeline for downstream SPSD-RL benchmark results.

Downloads last month: 9

Safetensors

Model size

4B params

Tensor type

F32

Model tree for LorMolf/SPSD-RL-Qwen3-4B-Factory-MHTrue-2Ep-20260608

Base model

Qwen/Qwen3-4B-Base

Finetuned

(463)

this model

LorMolf
/

SPSD-RL-Qwen3-4B-Factory-MHTrue-2Ep-20260608

Qwen3-4B SPSD-RL LLaMA-Factory SFT, mask_history=true, 2 epochs

Training

Notes

Model tree for LorMolf/SPSD-RL-Qwen3-4B-Factory-MHTrue-2Ep-20260608

Dataset used to train LorMolf/SPSD-RL-Qwen3-4B-Factory-MHTrue-2Ep-20260608