Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,51 @@
+---
+base_model: Jackrong/Qwopus3.5-9B-v3
+tags: [qwen3.5, reasoning, grpo, dapo, dpo, self-correction, verifiable-rewards]
+license: apache-2.0
+language: [en]
+pipeline_tag: text-generation
+---
+
+# Qwopus3.5-9B-v4
+
+## Training Pipeline
+```
+Jackrong/Qwopus3.5-9B-v3 (clean base, 87.8% HumanEval)
+ -> Phase 1: DAPO-GRPO 150 steps (native TRL)
+     loss=dapo, clip=[0.2, 0.28], beta=0, mask_truncated=True
+     Multiplicative reward: tags as entry fee, filler penalized
+ -> Phase 2: SAI-DPO Round 1 (self-generated pairs)
+ -> Phase 3: SAI-DPO Round 2 (hard example mining)
+ -> This Model
+```
+
+## Key Innovation: Multiplicative Rewards
+- No tags + correct = score x 0.1 (format bad)
+- Filler thinking = score x 0.15 - 0.5 (canned phrase penalty)
+- Full tags + correct = score x 1.0 + bonuses (full credit)
+
+## DAPO Features (Native TRL)
+- Token-level loss (no length bias)
+- Asymmetric clipping (epsilon_high=0.28)
+- Overlong filtering (mask_truncated=True)
+- No KL penalty (beta=0)
+
+## Files
+- `merged-model/` — Full merged safetensors
+
+## Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained(
+    'MeridianVector/Qwopus3.5-9B-v4', subfolder='merged-model',
+    torch_dtype='auto', device_map='auto', trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained(
+    'MeridianVector/Qwopus3.5-9B-v4', subfolder='merged-model', trust_remote_code=True)
+```
+
+## Acknowledgements
+- [Jackrong](https://huggingface.co/Jackrong) for Qwopus3.5-9B-v3
+- [Qwen Team](https://huggingface.co/Qwen) for Qwen3.5-9B
+
+Trained on Google Colab G4 (RTX PRO 6000 Blackwell, 96GB).
+Benchmarks pending.