Instructions to use zbeeb/Qwen2.5-Math-1.5B-OpenR1-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zbeeb/Qwen2.5-Math-1.5B-OpenR1-SFT with Transformers:

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

pipe = pipeline("text-generation", model="zbeeb/Qwen2.5-Math-1.5B-OpenR1-SFT")
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("zbeeb/Qwen2.5-Math-1.5B-OpenR1-SFT")
model = AutoModelForCausalLM.from_pretrained("zbeeb/Qwen2.5-Math-1.5B-OpenR1-SFT", 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 zbeeb/Qwen2.5-Math-1.5B-OpenR1-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zbeeb/Qwen2.5-Math-1.5B-OpenR1-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zbeeb/Qwen2.5-Math-1.5B-OpenR1-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zbeeb/Qwen2.5-Math-1.5B-OpenR1-SFT

SGLang

How to use zbeeb/Qwen2.5-Math-1.5B-OpenR1-SFT 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 "zbeeb/Qwen2.5-Math-1.5B-OpenR1-SFT" \
    --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": "zbeeb/Qwen2.5-Math-1.5B-OpenR1-SFT",
		"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 "zbeeb/Qwen2.5-Math-1.5B-OpenR1-SFT" \
        --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": "zbeeb/Qwen2.5-Math-1.5B-OpenR1-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zbeeb/Qwen2.5-Math-1.5B-OpenR1-SFT with Docker Model Runner:
```
docker model run hf.co/zbeeb/Qwen2.5-Math-1.5B-OpenR1-SFT
```

Qwen2.5-Math-1.5B-OpenR1-SFT

Built with Qwen. This is the final supervised fine-tuning checkpoint from a token-level study of the transition from SFT to GRPO. It was trained from Qwen/Qwen2.5-Math-1.5B at revision 4a83ca6e4526a4f2da3aa259ec36c259f66b2ab2. The model weights were modified through supervised fine-tuning; tokenizer and generation settings are the exact saved experiment artifacts.

Training

The SFT stage completed 278 optimizer updates and 20,016 examples, corresponding to one epoch over the selected subset of open-r1/OpenR1-Math-220k at revision e4e141ec9dea9f8326f4d347be56105859b2bd68.

The shared data selection uses complete, locally verified mathematical reasoning traces that fit every model's 4,096-token budget. Overlength examples were excluded rather than truncated. Prompt tokens are masked from the supervised loss. The repository includes the exact selected example IDs and probe IDs in data-selection.json.

Setting	Value
Framework	Prime RL 0.9.0
Prime RL revision	`ab5de8fff44b2c4a5c85e24b6e6e3f7d57eee7b1`
Seed	42
Sequence length	4096 tokens
Effective batch	72 examples
Learning rate	2e-05
Scheduler	cosine
Warmup ratio	0.03
Weight decay	0.01
Gradient clipping	1.0
Training precision	bf16
Saved tensor dtypes	F32

The independent cross-entropy calculation passed and the final fixed-probe evaluation completed. train_metrics.jsonl contains the training update metrics; evaluation-summary.jsonl contains the fixed train and held-out probe summaries, including teacher-forced and free-generation measurements. These are experiment probe results, not a claim of standard benchmark performance.

Use

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "zbeeb/Qwen2.5-Math-1.5B-OpenR1-SFT"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForCausalLM.from_pretrained(repo_id, dtype=torch.bfloat16, device_map="auto")
messages = [
    {"role": "system", "content": 'Please reason step by step, and put your final answer within \\boxed{}.'},
    {"role": "user", "content": "Solve 2x + 3 = 11."},
]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=3072, do_sample=True, temperature=0.6, top_p=0.95, eos_token_id=[151643, 151645], pad_token_id=151643)
print(tokenizer.decode(outputs[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Study collection and subsequent GRPO stage

This repository contains final SFT weights. Subsequent GRPO checkpoints are published separately in the same study collection after their runs finish. The subsequent RL dataset is zbeeb/Staleness-GRPO-DAPO-Math-17k.

provenance.json, training-config.json, and export-manifest.json record the source model and dataset revisions, training recipe, completion evidence, artifact sizes, and SHA-256 checksums. Optimizer states and recovery checkpoints are not included.

License and attribution

The upstream license is retained verbatim in LICENSE; attribution and modification notices are in Notice. This fine-tuned model is distributed under the same upstream model license.