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Model: RockToken/qwen3_30b_a3b_to_4b_onpolicy_5k_src30k-35k_cont
Source: Original Platform

README.md

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+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+language:
+- en
+base_model: RockToken/qwen3-30b-a3b-to-4b-onpolicy-10k
+datasets:
+- RockToken/openthoughts_prompt_math_5k_src30k-35k
+tags:
+- knowledge-distillation
+- on-policy-distillation
+- math
+- qwen3
+---
+
+# Qwen3-4B distilled from Qwen3-30B-A3B — On-policy 5k (continual, prompts 30k–35k)
+
+Continual on-policy knowledge distillation of a Qwen3-4B student toward the Qwen3-30B-A3B (MoE) teacher, on 5,000 math prompts from `openthoughts3_math_50k` (positions 30k–35k, disjoint from prior training slices).
+
+The student for this run is [RockToken/qwen3-30b-a3b-to-4b-onpolicy-10k](https://huggingface.co/RockToken/qwen3-30b-a3b-to-4b-onpolicy-10k), so the effective training exposure of this checkpoint is:
+
+1. **Off-policy KD** on 20k teacher responses (positions 0–20k of `openthoughts3_math_50k`)
+2. **On-policy KD** on 10k prompts (positions 20k–30k) — produced the 10k model
+3. **On-policy KD (this run)** on 5k prompts (positions 30k–35k) — this checkpoint
+
+Stages 2 and 3 use the same on-policy pipeline; only the seed weights, prompt slice, and step count differ.
+
+## Models
+
+| Role    | Model                                                  |
+|---------|--------------------------------------------------------|
+| Student | Qwen3-4B-Instruct-2507 (after stages 1–2 above)        |
+| Teacher | Qwen/Qwen3-30B-A3B-Instruct-2507 (MoE, ~3B active)     |
+
+`enable_thinking=False` throughout (both stages).
+
+## Training data
+
+- Source: `openthoughts3_math_50k` (math split of OpenThoughts-3)
+- Slice used here: prompts 30k–35k → [RockToken/openthoughts_prompt_math_5k_src30k-35k](https://huggingface.co/datasets/RockToken/openthoughts_prompt_math_5k_src30k-35k)
+- 5,000 single-user-turn prompts, mean length 261 chars, p99 845 chars
+
+## Training setup
+
+Framework: [KDFlow](https://github.com/wanng-ide/KDFlow) — FSDP2 + SGLang rollout, Ray-orchestrated GPU co-location with sleep/wakeup.
+
+Hardware: 1× node, 4× H100 (80 GB), ~34 h wall-clock (~136 GPU-hours).
+
+### Key hyperparameters (on-policy stage; same as stage 2)
+
+| Group           | Value                          |
+|-----------------|--------------------------------|
+| Backend         | fsdp2, bf16, gradient ckpt on  |
+| Epochs          | 1                              |
+| Train batch     | 4 (micro 1)                    |
+| Learning rate   | 2e-6, cosine, warmup 5%        |
+| KD ratio        | 1.0                            |
+| KD loss         | reverse KL (`rkl`)             |
+| KD algorithm    | `vanilla_kd`                   |
+| Temperature (KD)| 1.0                            |
+| Rollout engine  | SGLang, TP=2, 1 engine         |
+| Rollout batch   | 2 prompts × 4 samples/prompt   |
+| `generate_max_len` | 8000                        |
+| `prompt_max_len`   | 800  (total `max_len` 8800) |
+| Sampling        | temperature 1.0, top-p 1.0     |
+| Teacher         | TP=4, sleep/wakeup enabled     |
+
+Off-policy stage 1 hyperparameters (for context): batch 128, LR 2e-5, `kd_ratio` 0.5, `kd_loss` KL, `max_len` 16384, packing_samples on, ring attention.
+
+## Intended use
+
+Research on distillation dynamics. Domain skew is math (OpenThoughts-3 math split).
+
+## Limitations
+
+- Trained end-to-end on math prompts only; not tuned for chat, safety, or non-math domains.
+- `enable_thinking=False` — this student does not emit `<think>` traces.
+- Continual training may drift capabilities relative to the base checkpoint; see the wandb curves for loss dynamics.
+
+## Weights & Biases
+
+`legendary-music-16` (stage 2, 10k) and `lemon-sound-17` / `2gvd60dw` (this run, stage 3) under project `KDFlow`, entity `zhao666`.