Model-Optimizer/examples/speculative_decoding

Speculative Decoding

Speculative decoding accelerates auto-regressive generation in large language models (LLMs) by leveraging a lightweight draft model to predict the next γ tokens. The main LLM then verifies these candidate tokens in a single forward pass. If the draft model correctly predicts α tokens, the LLM can accept and generate α+1 tokens per verification step, significantly improving generation speed.

This folder contains an end-to-end runnable speculative decoding fine‑tuning pipeline in which Llama‑3.2‑1B (Hugging Face) is trained on the Daring-Anteater dataset.

This example focuses on training with Hugging Face. To train with Megatron‑LM, see the Megatron‑LM example.

Contents

Section	Description	Jump To
Pre-Requisites	Required & optional dependencies	[Link]
Simplified Workflow	Train, evaluate, and export EAGLE model with one-line command	[Link]
Online Training	Train draft model alongside base model in GPU memory	[Link]
Offline Training	Train draft model using pre-computed hidden states	[Link]
Streaming Training	Train draft on hidden states streamed from a live vLLM serve (no disk dump)	[Link]
After Training	Evaluation, export and deployment	[Link]
Advanced Usage	Data synthesis, vocab compression, and configuration	[Link]
Support Matrix	Supported models for speculative decoding training	[Link]
Speculation Module Checkpoints	View pre-trained speculation modules ready to deploy!	[Link]
Resources	Extra links to relevant resources	[Link]

Pre-Requisites

Docker

Please use the PyTorch docker image (e.g., nvcr.io/nvidia/pytorch:25.08-py3) or visit our installation docs for more information.

Also follow the installation steps below to upgrade to the latest version of Model Optimizer and install dataset and example-specific dependencies.

Local Installation

Install Modelopt with hf dependencies and other requirements for this example:

pip install -U nvidia-modelopt[hf]
pip install -r requirements.txt

Data Preparation

We support a range of input datasets. In this example, we will use the Daring-Anteater dataset.

python ../dataset/make_dataset.py -f ../dataset/example_data_config.yaml --full-conversations

See other-datasets section for other dataset options and instruction for user-provided data.

Omit --full-conversations if you plan to run synthetic data generation (see data-synthesis).

For large-scale training with NVIDIA's Nemotron datasets, use the dedicated scripts described in Nemotron Datasets.

Getting Started: Simplified Workflow

bash train_eagle3_and_export.sh --base_model meta-llama/Llama-3.2-1B-Instruct

This one-line command runs a minimal example workflow of training and exporting an EAGLE draft model in Modelopt. Specifically, it

• Initializes the draft model with default settings
• Fine-tunes the model on the dataset
• Evaluates the acceptance rate on MT-Bench
• Exports a checkpoint ready for deployment

Training Draft Model with Online Base Model

For small base models that fit in GPU memory, we can collocate them with draft models and train with the following command:

./launch_train.sh \
    --config ../../modelopt_recipes/general/speculative_decoding/eagle3.yaml \
    model.model_name_or_path=meta-llama/Llama-3.2-1B-Instruct \
    data.data_path=input_conversations/train.jsonl \
    training.output_dir=ckpts/llama-3.2-1b-online

All default training settings are in eagle3.yaml. You can adjust them by editing the YAML file or by specifying command-line overrides with OmegaConf dotlist arguments.

To enable context parallelism for long-context training, add training.cp_size=<N>. The saved modelopt checkpoint is similar in architecture to HF models. It can be further optimized through ModelOpt, e.g., PTQ and QAT.

Training Draft Model with Offline Base Model

For large models, you can export intermediate hidden states to disk and train only the draft model. This significantly reduces GPU memory requirements, but requires several to tens of terabytes of disk storage depending on dataset size.

Dumpping Hidden States to Disk

We support two backends for generating base model hidden states. For better effciency, it is recommended to use TRT-LLM:

python collect_hidden_states/compute_hidden_states_trtllm.py \
            --model $BASE_MODEL \
            --input-file input_conversations/train.jsonl \
            --output-dir $HIDDEN_STATES_DIR

NOTE: TRT-LLM installation needed for the above command.

Alternatively, you can generate the same hidden states with HF:

python collect_hidden_states/compute_hidden_states_hf.py \
            --model $BASE_MODEL \
            --input-file input_conversations/train.jsonl  \
            --output-dir $HIDDEN_STATES_DIR

NOTE: See run_hf_compute_hiddens_dp.sh and run_trtllm_compute_hiddens_dp.sh for a simple example using data parallelism (DP) to accelerate hidden state generation.

Train Draft Model with Dumped Hidden States

Once we finish dumping hidden states, launch offline training pointing to the hidden states directory:

./launch_train.sh \
    --config ../../modelopt_recipes/general/speculative_decoding/eagle3.yaml \
    model.model_name_or_path=meta-llama/Llama-3.2-1B-Instruct \
    data.offline_data_path=$HIDDEN_STATES_DIR \
    training.output_dir=ckpts/llama-3.2-1b-offline

Training Draft Model with Streaming Base Model

For large base models, you can stream hidden states from a live vllm serve instead of dumping them to disk: a co-located server produces the base-model hidden states on the fly and sends them to the trainer over NIXL RDMA, scaling to multiple nodes (dedicated serve replicas + DDP trainers). See the launcher examples, e.g. Kimi-K2.5 streaming EAGLE3 and streaming DFlash.

Model Validation

For online training checkpoints, we can run in-framework evaluation on MT-bench:

python scripts/ar_validate.py --model_path $ONLINE_CKPT

Note: In-framework evaluation is supported only for online training. For offline training checkpoints, please export the model and evaluate it using serving frameworks.

Export

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.

Deployment

The exported checkpoint can be deployed on TRT-LLM or SGLang.

TRT-LLM

To serve the checkpoint with TRT-LLM, run trtllm-serve with:

trtllm-serve <base_model_checkpoint> --host 0.0.0.0 --port 8000 --backend pytorch --max_batch_size 32 --max_num_tokens 8192 --max_seq_len 8192 --extra_llm_api_options extra-llm-api-config.yml

, with extra-llm-api-config.yml being

enable_attention_dp: false
disable_overlap_scheduler: true
enable_autotuner: false

cuda_graph_config:
    max_batch_size: 1

speculative_config:
    decoding_type: Eagle
    max_draft_len: 3
    speculative_model_dir: <draft_model_checkpoint>

kv_cache_config:
    enable_block_reuse: false

Please refer to TRT-LLM Doc: Speculative Decoding for detailed usage.

vLLM

Please refer to VLLM Doc: Speculative Decoding for detailed usage.

Optionally, you can convert the exported checkpoint to contain target model information, which is accepted by vLLM to simplify depployment:

python scripts/convert_to_vllm_ckpt.py --input <exported_ckpt> --verifier <target_model> --output <output_dir>

SGLang

Please refer to SGLang Doc: Speculative Decoding for detailed usage.

SpecDec Bench

One can also use examples/specdec_bench to validate the trained Eagle3 checkpoints in a variety of frameworks (vLLM, SGLang, TRT-LLM) on a set of datasets.

Deploying Quantized model

See more details on deployment of quantized model to TRTLLM here.

Advanced Usage

Other Datasets

In addition to the default dataset, we support adding several other commonly used datasets in ../dataset/make_dataset.py:

• MTBench (for debugging)
• ShareGPT
• Magpie (Full 1M, and 500k and 300k filtered)
• Nemotron Post-Training Dataset V2

To use your own datasets, please preprocess your data into a .jsonl file with each line in the format:

{
    "messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]
}

Nemotron Datasets

For large-scale training we provide dedicated scripts for NVIDIA's Nemotron Post-Training dataset collections. Both scripts support two modes:

• generate (default) — strips all assistant turns, producing a conversation skeleton (system + user turns only) for synthetic data generation. The downstream pipeline feeds these to the target model turn-by-turn, appending each generated response before sending the next user turn. Optional augmentation adds language-redirect and style-hint variants to diversify prompts.
• train — keeps all turns in clean OpenAI message format (role + content) for direct SFT training. Prompt-only rows are dropped. Tool-call context (tool_calls, tool_call_id) is preserved for agentic datasets.

Nemotron Post-Training Dataset V2 (nvidia/Nemotron-Post-Training-Dataset-v2):

# Synthetic data generation (~3.3M rows):
python ../dataset/make_nemotron_ptv2_dataset.py --output-dir /tmp/ptv2_gen

# Direct SFT training mix (~1.9M rows):
python ../dataset/make_nemotron_ptv2_dataset.py --mode train --output-dir /tmp/ptv2_train

Covers: stem, chat, math, code + 5 multilingual splits (ja/de/it/es/fr, capped at 100K each).

Nemotron Post-Training V3 collection (16 datasets):

# Synthetic data generation (~3.4M rows):
python ../dataset/make_nemotron_ptv3_dataset.py --output-dir /tmp/ptv3_gen

# Direct SFT training mix (~3.9M rows, includes agentic/tool-use datasets):
python ../dataset/make_nemotron_ptv3_dataset.py --mode train --output-dir /tmp/ptv3_train

Covers: math, code, science, instruction-following, agentic/tool-use, safety, finance, and multilingual data. The dataset mix and per-split row caps are configurable via ../dataset/nemotron_ptv3_datasets.yaml.

Augmentation (generate mode only) is controlled by ../dataset/augmentations.yaml. By default it includes 12 language-redirect variants and several style/format hints. The /no_think system-prompt variant is disabled by default (enable it for models that support it, e.g. Qwen3):

# Custom augmentation config:
python ../dataset/make_nemotron_ptv2_dataset.py \
    --augmentations-config my_augs.yaml --output-dir /tmp/ptv2_gen

Data Synthesis

To achieve higher acceptance rates during speculative decoding, it is beneficial to use conversations generated by the base model as training data. This ensures that the draft model's output distribution closely aligns with that of the base model.

First, prepare input conversation skeletons using --mode generate (default) from the Nemotron scripts above, or with make_dataset.py (omitting --full-conversations). Then launch an inference server with the base model:

pip install vllm
vllm serve meta-llama/Llama-3.2-1B-Instruct --api-key token-abc123 --port 8000 --generation-config vllm --tensor-parallel-size 1

Note: Add --quantization=modelopt flag for quantized models.

The --generation-config vllm server option avoids a checkpoint-specific output cap.

Then, we generate conversations with the base model using the prepared prompts:

python scripts/server_generate.py --data_path input_conversations/train.jsonl --output_path synthetic/train.jsonl

Inputs can use conversations or messages, with either full conversations or user-only skeletons. Every user turn gets a fresh response using the previously generated responses as history. Input system messages are preserved; --system_prompt <system_prompt_text> overrides them and emits a warning when an input system message is replaced. Output remains one full conversation per JSONL record, with no train/eval/test split or expansion into separate assistant-turn examples. Existing output IDs are skipped on resume.

Use --extra_body to pass model-specific chat parameters, including thinking controls and sampling settings. For example, with a compatible Qwen server and reasoning parser:

python scripts/server_generate.py --data_path input_conversations/train.jsonl \
    --output_path synthetic/train.jsonl --model qwen3.8-27b --temperature 1.0 --max_tokens 8192 \
    --extra_body '{"reasoning_effort":"medium","top_p":0.95,"top_k":20,"chat_template_kwargs":{"enable_thinking":true,"preserve_thinking":true}}'

The client uses the server's default thinking mode and effort unless overridden through --extra_body. Returned reasoning is saved in the assistant's reasoning_content field and included in subsequent requests. Token-capped conversations retain the existing truncated: true flag and need filtering before training. Failed conversations and partial answers are excluded from training output. Generation continues after per-conversation failures and records each one in <output_path>.failures, a JSONL journal whose filename deliberately does not end in .jsonl so the shard combiner ignores it.

Rerun the same command with the same input ordering and output path to resume. Completed conversation IDs are skipped. Connection errors, timeouts, rate limits, and temporary server errors remain retryable on resume. Empty final answers also remain retryable when --temperature is greater than zero. At temperature zero, empty final answers are recorded as rejected to avoid repeating greedy-generation failures. Other rejected inputs and responses, including unsupported tool roles or calls and HTTP 400/422 responses, are skipped on ordinary resume. Inspect the journal and use --retry_failed to retry these rejections after correcting the input or request settings. This script has no tool-execution loop.

Use --fail_on_error to return a nonzero exit status if failures remain after the batch finishes. Authentication failures, missing endpoints or models, and unexpected internal or output-write errors still exit nonzero by default. When --log_empty_conversations is enabled, the finished marker means every conversation is either saved or recorded as rejected; no marker is appended while retryable failures remain. Always inspect the failure journal before using the generated data.

For chat generation, --max_tokens 0 sends no fixed response cap; the server determines the budget from its configured context window and generation defaults. Increase --request_timeout (seconds, default 600) when long responses need more time.

For large scale data generation, please see SLURM prepare data for SLURM support.

Configuring Draft Model

For EAGLE‑1 and EAGLE‑3 we provide a default model architecture config in ModelOpt. You can override default settings via eagle.eagle_architecture_config in the YAML. E.g. to use a 2-layer EAGLE head with 8192 intermediate size:

eagle:
  eagle_architecture_config:
    num_hidden_layers: 2
    intermediate_size: 8192

Draft Vocabulary Compression

We can optionally use smaller vocab size for the draft model for faster training and inference. E.g. Llama3.2-1B has a vocab size of 128256. In this example, we construct a draft vocab mapping of size 32k by finding the most commonly appeared vocabs in our training set:

python scripts/calibrate_draft_vocab.py --model meta-llama/Llama-3.2-1B-Instruct --data input_conversations/train.jsonl --draft_vocab_size 32000 --save_dir draft_vocab_cache

This will produce a d2t.pt file in save_dir, which is the mapping from draft token to target token. During inference, draft tokens can be mapped back to target tokens by target_token = draft_token + d2t[draft_token].

Then, set eagle_architecture_config.draft_vocab_size: 32000 and data.draft_vocab_cache: <path_to_d2t.pt> in your YAML. The draft model will use this provided vocab table during training and export.

Interact with modelopt.torch.speculative

main.py provides a complete example for converting a HF base model for speculative decoding and training it. The core steps are loading the base model, converting it with an eagle config dict, and training with HF Trainer:

import modelopt.torch.speculative as mtsp

# Convert base model in-place to an EAGLE speculative decoding model
eagle_cfg = {"eagle_decoder_type": "llama", ...}  # fields from EagleConfig
mtsp.convert(model, [("eagle", eagle_cfg)])

# Train with HF Trainer as usual
trainer = transformers.Trainer(model=model, ...)
trainer.train()
trainer.save_model("<output_dir>")

See main.py for the full example including tokenizer setup, dataset loading, and checkpoint handling.

Support Matrix

Model	Medusa	EAGLE1/2	EAGLE3
LLAMA 2	✅	✅	✅
LLAMA 3, 3.1	✅	✅	✅
Mistral	✅	✅	✅
Phi 3	✅	✅	✅
QWen 1.5,2,2.5,3	✅	✅	✅
Kimi-K2.5, K2.6			✅

Speculation Module Checkpoints

Ready-to-deploy speculation module checkpoints [🤗 Hugging Face - NVIDIA Speculative Decoding Modules Collection] Deployable on TensorRT-LLM and SGLang!
More models coming soon!

Resources

• 📅 Roadmap
• 📖 Documentation
• 🎯 Benchmarks
• 💡 Release Notes
• 🐛 File a bug
• ✨ File a Feature Request

DFlash (Block Diffusion for Speculative Decoding)

DFlash is a parallel speculative decoding method based on Block Diffusion. Unlike autoregressive draft models (EAGLE3), DFlash predicts an entire block of tokens in a single forward pass using masked parallel prediction with KV injection from the target model's hidden states.

Quick Start

For a complete end-to-end example (training + evaluation), see the launcher example:

uv run launch.py --yaml examples/Qwen/Qwen3-8B/hf_online_dflash.yaml --yes

Key Configuration (dflash.yaml)

Field	Default	Description
dflash.dflash_block_size	8	Block size for parallel prediction
dflash.dflash_num_anchors	512	Number of anchor positions per sample
dflash.dflash_loss_decay_factor	4.0	Exponential decay gamma (0 disables)
dflash.dflash_self_logit_distillation	true	Use logit distillation from target
dflash.dflash_architecture_config.num_hidden_layers	5	Draft decoder layers
dflash.dflash_architecture_config.mask_token_id	auto	Token ID for masked positions
training.answer_only_loss	false	Mask loss on non-assistant tokens

Qwen3 sliding window attention is automatically supported — draft layers inherit layer_types and sliding_window from the config, matching the target model's attention pattern.

Export

python scripts/export_hf_checkpoint.py \
    --model_path /path/to/training/output \
    --export_path /path/to/exported/model

Results

See doc/dflash.md for design details, benchmark results, and open items.