Instructions to use jaygala24/Qwen2.5-3B-ReMax-math-reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jaygala24/Qwen2.5-3B-ReMax-math-reasoning with Transformers:

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

pipe = pipeline("text-generation", model="jaygala24/Qwen2.5-3B-ReMax-math-reasoning")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("jaygala24/Qwen2.5-3B-ReMax-math-reasoning")
model = AutoModelForMultimodalLM.from_pretrained("jaygala24/Qwen2.5-3B-ReMax-math-reasoning")
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 jaygala24/Qwen2.5-3B-ReMax-math-reasoning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jaygala24/Qwen2.5-3B-ReMax-math-reasoning"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jaygala24/Qwen2.5-3B-ReMax-math-reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jaygala24/Qwen2.5-3B-ReMax-math-reasoning

SGLang

How to use jaygala24/Qwen2.5-3B-ReMax-math-reasoning 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 "jaygala24/Qwen2.5-3B-ReMax-math-reasoning" \
    --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": "jaygala24/Qwen2.5-3B-ReMax-math-reasoning",
		"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 "jaygala24/Qwen2.5-3B-ReMax-math-reasoning" \
        --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": "jaygala24/Qwen2.5-3B-ReMax-math-reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jaygala24/Qwen2.5-3B-ReMax-math-reasoning with Docker Model Runner:
```
docker model run hf.co/jaygala24/Qwen2.5-3B-ReMax-math-reasoning
```

Qwen2.5-3B-ReMax-math-reasoning

File size: 3,847 Bytes

---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-3B
tags:
- reinforcement-learning
- remax
- math-reasoning
- pipelinerl
datasets:
- gsm8k_train
- math_train
pipeline_tag: text-generation
---

# Qwen2.5-3B-ReMax-math-reasoning

This model is a fine-tuned version of [Qwen2.5-3B](https://huggingface.co/Qwen/Qwen2.5-3B) using **ReMax without KL penalty** for mathematical reasoning.

Trained with [PipelineRL](https://github.com/ServiceNow/PipelineRL).

## Training Details

### Datasets

| Split | Datasets |
|-------|----------|
| Train | `gsm8k_train`, `math_train` |
| Test  | `gsm8k_test`, `math_500` |

### RL Algorithm

| Parameter | Value |
|-----------|-------|
| Algorithm | ReMax |
| Advantage Baseline | Greedy-decoded response reward |
| Extra Inference | 1 deterministic rollout per prompt |
| Group Structure | Not required |
| Policy Loss | `ppo` |
| KL Coefficient | `0.0` |
| Epsilon (clip) | `0.2` |
| Discount Factor (`gamma`) | `1.0` |
| Divide Advantage by Std | `False` |
| Filter Zero Advantage Groups | `False` |
| Rollouts per Problem | `16` |

ReMax uses a greedy-decoded response's reward as the baseline for advantages.

### Training Hyperparameters

| Parameter | Value |
|-----------|-------|
| Base Model | `Qwen/Qwen2.5-3B` |
| Learning Rate | `1e-06` |
| LR Scheduler | `cosine` |
| Warmup Steps | `25` |
| Max Training Steps | `1500` |
| Micro Batch Size | `2` |
| Gradient Accumulation | `128` |
| Effective Batch Size | `256` |
| Sequence Length | `8192` |
| Gradient Clipping | `0.3` |
| Weight Decay | `0.01` |
| Optimizer | `adamw_torch` |
| Precision | `bf16` |
| DeepSpeed | ZeRO Stage 3 |

## Evaluation Results

Pass@k on math reasoning benchmarks (N=32 samples per problem, temperature=1.0):

| Dataset | pass@1 | pass@2 | pass@4 | pass@8 | pass@16 | pass@32 |
| --- | ---: | ---: | ---: | ---: | ---: | ---: |
| GSM8K (test) | 85.99 | 90.50 | 93.34 | 95.29 | 96.64 | 97.50 |
| MATH-500 | 67.36 | 74.99 | 81.23 | 85.92 | 89.09 | 91.20 |
| **Overall** | **80.87** | **86.24** | **90.01** | **92.71** | **94.56** | **95.77** |

*GSM8K test: 1319 problems · MATH-500: 500 problems · Overall: 1819 problems (overall weighted by problem count).*

## Training Curves

![Training Metrics](training_metrics.png)

## W&B Run

Full training logs: [https://wandb.ai/jaygala24-team/rl-post-training/runs/qwen2.5_3b_remax_3a1f_4xh100_214753_finetune_1c7d72aa](https://wandb.ai/jaygala24-team/rl-post-training/runs/qwen2.5_3b_remax_3a1f_4xh100_214753_finetune_1c7d72aa)

## Usage

### Transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("jaygala24/Qwen2.5-3B-ReMax-math-reasoning", revision="step-0200")  # optional branch, e.g. "step-0400"
tokenizer = AutoTokenizer.from_pretrained("jaygala24/Qwen2.5-3B-ReMax-math-reasoning", revision="step-0200")

prompt = "Please reason step by step, and put your final answer within \\boxed{}.\n\nWhat is the sum of 123 and 456?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=4096, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### vLLM

```python
from vllm import LLM, SamplingParams

llm = LLM(model="jaygala24/Qwen2.5-3B-ReMax-math-reasoning", revision="step-0200")  # optional branch, e.g. "step-0400"
sampling_params = SamplingParams(temperature=0.7, max_tokens=4096)

prompt = "Please reason step by step, and put your final answer within \boxed{}.

What is the sum of 123 and 456?"
outputs = llm.generate([prompt], sampling_params)
print(outputs[0].outputs[0].text)
```

## Framework

- [PipelineRL](https://github.com/ServiceNow/PipelineRL)
- [Transformers](https://github.com/huggingface/transformers)
- [DeepSpeed](https://github.com/microsoft/DeepSpeed) (ZeRO Stage 3)