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Model: willhx/Qwen3-8B-Base-Math-SeaSFT-Search-EOPD-Tau
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-8B-Base
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- qwen3
+- on-policy-distillation
+- eopd
+- search
+- tool-use
+- agent
+---
+
+# Qwen3-8B-Base-Math-SeaSFT-Search-EOPD-Tau
+
+An 8B agentic model trained with **Entropy-Aware On-Policy Distillation (EOPD)** in a
+multi-stage RL pipeline, built on **Qwen3-8B-Base**. This checkpoint is the final iteration
+(`iter_0000300`) of the Search-domain EOPD run.
+
+## Training pipeline
+
+| Stage | Description |
+|-------|-------------|
+| Base  | `Qwen/Qwen3-8B-Base` |
+| SFT   | Math + Search cold-start SFT, then Tau (tool-agent) SFT — student init `Qwen3-8B-Base-Math-SeaSFT-Search-TauSFT-Tau` |
+| EOPD  | On-policy distillation from a **Search specialist teacher** (`Qwen3-8B-Base-Math-SeaSFT-Search`) |
+
+## Method: Entropy-Aware On-Policy Distillation (EOPD)
+
+EOPD combines two KL directions to get the best of mode-seeking and mode-covering distillation:
+
+- **Reverse-KL OPD** everywhere (mode-seeking): the sampled-token log-ratio
+  `student_logp − teacher_logp` is subtracted from the advantage, sharpening the student
+  toward the teacher's modes.
+- **Forward-KL on high-entropy teacher tokens** (mode-covering): where reverse KL alone
+  collapses diversity, a differentiable forward-KL loss
+  `KL(p̃_teacher_topk ‖ p̃_student_topk)` is added, gated by the teacher's per-token entropy
+  (`1[H_teacher > τ]`) and computed over the teacher's renormalized top-k (k=16) distribution.
+
+The teacher is served with SGLang and returns both the scalar sampled-token logprob (reverse
+KL) and the per-token top-k distribution + entropy (forward KL). Training was done with the
+[slime](https://github.com/THUDM/slime) RL framework on Megatron-LM; this repo is the
+Hugging Face `safetensors` export of the final checkpoint.
+
+## Architecture
+
+Identical to Qwen3-8B-Base: 36 layers, hidden size 4096, 32 attention heads, 8 KV heads (GQA),
+head dim 128, QK-LayerNorm, RMSNorm, SwiGLU, RoPE (θ=1e6), 151,936 vocab, 32,768 context.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "willhx/Qwen3-8B-Base-Math-SeaSFT-Search-EOPD-Tau"
+tok = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="bfloat16", device_map="auto")
+
+prompt = "Who won the Nobel Prize in Physics in 1921?"
+inputs = tok(prompt, return_tensors="pt").to(model.device)
+out = model.generate(**inputs, max_new_tokens=256)
+print(tok.decode(out[0], skip_special_tokens=True))
+```
+
+The model is trained for a search/tool-use agent loop (Search-R1 style retrieval); to reproduce
+the agentic behavior, drive it with the same tool prompt/format used during training.
+
+## License
+
+Apache-2.0, inherited from Qwen3-8B-Base.