Drop unverifiable claims from the external-draft docs

examples/speculative_decoding/README.md

@@ -137,8 +137,7 @@ For large base models, you can stream hidden states from a live `vllm serve` ins
 
 The modes above graft a draft head onto the target. The `external` mode instead
 trains an ordinary pretrained causal LM as the draft: it keeps its own embeddings
-and lm_head, the target is never modified, and the result exports as a plain HF
-checkpoint that TRT-LLM, vLLM and SGLang load with no mode-specific support.
+and lm_head, the target is never modified, and the result is a plain HF checkpoint.
 
 Dump the target's top-k deployment policy once, then train against it:
 
@@ -174,7 +173,9 @@ python scripts/ar_validate.py --model_path $ONLINE_CKPT
 python scripts/export_hf_checkpoint.py --model_path $OUTPUT_DIR --export_path $EXPORT_PATH
 ```
 
-This exports the model from a ModelOpt checkpoint to a deployment-compatible format.
+This exports the model from a ModelOpt checkpoint to a deployment-compatible format. It
+applies to the grafted-head modes above; an external draft needs no export pass, since its
+training output is already a plain HF checkpoint.
 
 ## Deployment
 

examples/speculative_decoding/doc/external_draft.md

@@ -3,7 +3,7 @@
 An external draft is an ordinary pretrained causal LM trained to propose tokens for a
 target model. Unlike the head-based modes (EAGLE, Medusa, DFlash) nothing is grafted onto
 the target: it contributes its vocabulary and, on the dense path, its `lm_head`. The result
-exports as a plain HuggingFace checkpoint.
+is a plain HuggingFace checkpoint.
 
 Recipe: [`modelopt_recipes/general/speculative_decoding/external_draft.yaml`](../../../modelopt_recipes/general/speculative_decoding/external_draft.yaml) |
 Swap implementation: [`vocab_swap.py`](../../../modelopt/torch/speculative/external/vocab_swap.py)
@@ -173,10 +173,10 @@ The run reports what carried over, typically a minority of the table and nearly
 most frequent ids, since the rare tail is where tokenizers disagree.
 
 > **Note on judging a swap:** the match percentage describes the swap, not the draft.
-> Overlap figures far apart have produced similar acceptance. Measure acceptance on held-out
-> data before committing to a swapped draft, and prefer a vocabulary-matched draft where one
-> exists: the replacement matrix is `base_vocab_size × hidden_size` parameters, a large
-> fraction of a small draft, and a swapped draft stays behind a matched one of similar size.
+> Overlap figures far apart have behaved similarly, so do not read the coverage number as a
+> quality score. Prefer a vocabulary-matched draft where one exists: the replacement matrix
+> is `base_vocab_size × hidden_size` parameters, a large fraction of a small draft, and a
+> swapped draft stays behind a matched one of similar size.
 
 A swapped draft starts with a large untrained fraction and wants a higher learning rate than
 a matched draft of comparable size.
@@ -193,6 +193,15 @@ Training writes an ordinary HuggingFace checkpoint, shipped with the **target's*
 when the vocabulary was swapped, since the draft's rows are indexed by the target's ids from
 that point on. Nothing in the saved artifact depends on ModelOpt at load time.
 
+**There is no export step for this mode.** `scripts/export_hf_checkpoint.py` restructures a
+grafted head into a servable checkpoint; an external draft has no head to restructure, so the
+script rejects it. The training output is the artifact.
+
+`training.output_dir` also holds training residue that `from_pretrained` ignores:
+`modelopt_state.pth`, `trainer_state.json`, `training_args.bin` and the `checkpoint-*`
+directories. Ship `config.json`, `generation_config.json`, the weights and the tokenizer
+files.
+
 ## Reference Configuration (Llama-3.2-1B drafting for Qwen3.8-27B)
 
 A cross-tokenizer draft: Llama-3.2-1B-Instruct re-indexed onto Qwen3.8-27B's
@@ -240,16 +249,16 @@ torchrun --nproc_per_node 8 main.py \
 | Epochs | 1 |
 | Serving policy | top-k 20, top-p 0.95, temperature 1.0 |
 
-> **Note on the learning rate:** we recommend sweeping against held-out acceptance to find
-> the best rate for your own draft and target.
+> **Note on the learning rate:** we recommend sweeping to find the best rate for your own
+> draft and target.
 
 ## Open Items
 
 ### Validated end-to-end
 
-- Sparse teacher-policy training with `tvd`, from dump through training to an exported
-  checkpoint, with acceptance measured on held-out conversations.
-- The cross-tokenizer vocabulary swap, on a draft trained and measured this way.
+- Sparse teacher-policy training with `tvd`, from dump through training to a loadable
+  checkpoint.
+- The cross-tokenizer vocabulary swap, on a draft trained this way.
 
 ### Covered by tests, not yet exercised in a full run
 
@@ -262,5 +271,3 @@ torchrun --nproc_per_node 8 main.py \
 - Streaming the teacher data from a live `vllm serve`. It is accepted for this mode but has
   not been exercised. Online training is rejected outright, since it produces no cached
   target outputs for the draft to be scored against.
-- VLM targets. `main.py` routes through `load_vlm_or_llm`, so the path exists, but no
-  external draft has been trained against one.