---
license: apache-2.0
base_model: Qwen/Qwen3-8B-Base
pipeline_tag: text-generation
library_name: transformers
tags:
- on-policy-distillation
- multi-teacher
- qwen3
---

# mopd-sdft-iter100

Qwen3-8B student trained with **multi-teacher On-Policy Distillation (OPD)** over a mixed
math + search + tau (tool-use) rollout stream. This is the **iteration-100** checkpoint of the
light-SFT-student variant.

## Training

- **Base / init:** `Qwen/Qwen3-8B-Base`, warm-started from a light multi-task **oracle-mix SFT** checkpoint (iter 500) before OPD.
- **Method:** On-policy distillation — the only training signal is per-token reverse-KL between the student and a **domain-specific teacher**, selected per sample by a static domain tag. Task reward is 0 (pure distillation).
- **Domains / teachers:** math, search (retrieval-augmented), and tau (agentic tool-use), each with its own teacher model.
- **Checkpoint:** iteration 100.

This is a sibling of `willamazon1/mopd-sdft-iter200` (same run, later iteration) and
`willamazon1/mopd-iter200` (full Math→Sea→Tau→IF SFT-chain student, same OPD reward).

## Architecture

Qwen3-8B dense: 36 layers, hidden 4096, FFN 12288, 32 query / 8 KV heads (GQA), head_dim 128,
QK-layernorm, untied embeddings, RoPE θ=1e6, vocab 151936, context 32768.

## Usage

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("willamazon1/mopd-sdft-iter100")
model = AutoModelForCausalLM.from_pretrained("willamazon1/mopd-sdft-iter100", dtype=torch.bfloat16, device_map="auto")

msgs = [{"role": "user", "content": "What is 12*8?"}]
text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
ids = tok(text, return_tensors="pt").input_ids.to(model.device)
out = model.generate(ids, max_new_tokens=64, do_sample=False)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))
```

Converted from a Megatron `torch_dist` training checkpoint to HuggingFace safetensors.