Fix README (was empty): real accept_len + distribution-shift note

README.md

@@ -0,0 +1,64 @@
+---
+license: other
+base_model: moonshotai/Kimi-K2.6
+tags:
+- text-generation
+- speculative-decoding
+- eagle3
+- kimi-k2.6
+- mla
+- torchspec
+---
+
+# kimi-k2.6-eagle3-mla
+
+Eagle3 MTP draft model with MLA (Multi-Latent Attention) for accelerating
+inference of [Kimi-K2.6](https://huggingface.co/moonshotai/Kimi-K2.6).
+
+This is a fine-tuned draft, anchored to the official
+[lightseekorg/kimi-k2.6-eagle3-mla](https://huggingface.co/lightseekorg/kimi-k2.6-eagle3-mla)
+initialization. It targets multi-hop (downstream-position) acceptance while
+preserving the first-hop gain, evaluated by runtime accept-length on a frozen
+full-context held-out set.
+
+## Fine-tune setup
+
+- **Init**: lightseekorg/kimi-k2.6-eagle3-mla (official MLA weights)
+- **Objective**: Eagle3 distillation + multi-step TTT supervision
+  (`ttt_steps=4`, `ttt_step_loss_decay=1.0`, off-policy downstream tokens)
+- **Anti-over-specialization**: L2-SP weight-space anchor toward the init
+  (penalize trainable-param drift; lambda=1e-4)
+- **Optimizer**: lr 2e-5, cosine schedule
+- **Checkpoint**: best by held-out validation loss on the K2.6 ruler
+  (step 95400; val_loss 5.490, the global minimum of the v3 run)
+
+## Performance
+
+Primary metric is **accept_length** — average tokens accepted per speculation
+step with `num_speculative_tokens=3` (higher is better). Evaluated with
+vLLM 0.20.0 on 8x H200, TP=8, max-model-len 32768, greedy.
+
+On a frozen K2.6 full-context held-out judge set (914 prompts):
+
+| Model | accept_len |
+|-------|-----------:|
+| lightseek (official init) | 2.285 |
+| this model | **2.308** |
+
+This draft improves over the official init on the K2.6 held-out distribution.
+
+## Note on distribution shift
+
+This checkpoint is selected by validation loss on the K2.6 teacher
+distribution. In cross-version testing against real Kimi-K2.7-Code production
+traffic, the official lightseek init currently shows higher accept-length than
+this fine-tune — i.e. the K2.6 fine-tune over-specializes to its training
+distribution. If your serving traffic differs substantially from long
+multi-turn K2.6 dialogues, benchmark both this draft and the lightseek init on
+your own traffic before choosing. (The L2-SP anchor above is intended to
+mitigate this; tuning it against real-traffic accept-length is ongoing.)
+
+## Usage
+
+Serve with vLLM as the speculative draft for Kimi-K2.6, with
+`num_speculative_tokens=3` in the speculative-config.