初始化项目，由ModelHub XC社区提供模型

Model: jaygala24/Qwen2.5-3B-DAPO-math-reasoning Source: Original Platform
2026-05-02 18:17:51 +08:00
commit a9858ca450
16 changed files with 152644 additions and 0 deletions
--- a/README.md
+++ b/README.md
@@ -0,0 +1,123 @@
+---
+library_name: transformers
+license: apache-2.0
+base_model: Qwen/Qwen2.5-3B
+tags:
+- reinforcement-learning
+- dapo
+- math-reasoning
+- pipelinerl
+datasets:
+- gsm8k_train
+- math_train
+pipeline_tag: text-generation
+---
+
+# Qwen2.5-3B-DAPO-math-reasoning
+
+This model is a fine-tuned version of [Qwen2.5-3B](https://huggingface.co/Qwen/Qwen2.5-3B) using **DAPO (Decoupled Clip and Dynamic Sampling Policy Optimization) 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 | DAPO (Decoupled Clip and Dynamic Sampling Policy Optimization) |
+| Advantage Baseline | Group mean reward |
+| Extra Inference | None |
+| Group Structure | 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 | `True` |
+| Rollouts per Problem | `16` |
+
+DAPO extends GRPO with clip-higher (asymmetric PPO clipping), dynamic sampling (filtering zero-variance groups), token-level loss aggregation, and overlong reward shaping.
+
+### 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) | 86.52 | 91.04 | 93.73 | 95.52 | 96.73 | 97.50 |
+| MATH-500 | 70.66 | 77.91 | 83.26 | 87.27 | 90.10 | 92.00 |
+| **Overall** | **82.16** | **87.43** | **90.85** | **93.25** | **94.90** | **95.99** |
+
+*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_dapo_no_kl_3a1f_4xh100_235923_finetune_72e237c1](https://wandb.ai/jaygala24-team/rl-post-training/runs/qwen2.5_3b_dapo_no_kl_3a1f_4xh100_235923_finetune_72e237c1)
+
+## Usage
+
+### Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("jaygala24/Qwen2.5-3B-DAPO-math-reasoning", revision="step-0200")  # optional branch, e.g. "step-0400"
+tokenizer = AutoTokenizer.from_pretrained("jaygala24/Qwen2.5-3B-DAPO-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-DAPO-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)