Auto Recipe — Recipe Index & Recommendation#

This skill indexes every shipped recipe and helps users pick the right starting config, adjust parallelism, and avoid common pitfalls.

How to Use This Skill#

  1. Ask the user for: model name/size, GPU count & type, training goal (pretrain / SFT / PEFT), and sequence length (if non-default).

  2. Look up the best-match recipe in the index below.

  3. Recommend the recipe function name + entry-point command.

  4. Provide adjustment advice (parallelism resizing, batch tuning, pitfalls).

First Answer Checklist#

When recommending recipes, always include these distinctions before the long index details:

  1. Library recipes under src/megatron/bridge/recipes/ are for functional training and use scripts/training/run_recipe.py.

  2. Benchmark recipes under src/megatron/bridge/perf_recipes/ are for upper-bound throughput benchmarks. They own their canonical benchmark data and settings and should not be presented as production training recipes.

  3. For a first-time Bridge smoke test, recommend llama3_8b_pretrain_config with mock data via --dataset mock.

  4. For normal SFT recommendations, select a finetuning preset such as --dataset squad or --dataset tulu3; for pretrain and mock validation recommendations, use --dataset mock. Do not pair the pretraining-only mock preset with an SFT or PEFT mode.

  5. After the recipe and dataset, give the required resizing rules: TP must divide num_key_value_heads, keep TP within one node unless using NVL72-class interconnect, enable SP when TP > 1, configure CP for long context, DP is implicit, and reduce micro_batch_size first on OOM.

  6. State whether each proposed override changes the convergence contract or only the execution/performance mapping. Do not trade convergence semantics for throughput without calling it a new experiment.

Configuration Layers and Change Control#

Separate training semantics from their hardware mapping before recommending or tuning a recipe.

Convergence configuration includes the starting checkpoint and trainable parameters; dataset/revision/split/order/seeds; tokenizer, masking, truncation, and packing; sequence length; global batch and token budget; objective and loss coefficients; natural or forced MoE routing and token-dropping policy; optimizer, LR, schedule, warmup, betas, epsilon, weight decay, clipping, and dropout; arithmetic and optimizer-state precision; and PEFT adapter settings. Changing one of these creates a new convergence experiment.

Execution/performance configuration includes hardware count and topology; TP/PP/VP/CP/EP/ETP/DP/SP; recompute and offload; distributed optimizer/FSDP; communication overlap; fusions and attention backends; CUDA graphs and compilation; checkpoint I/O; and MoE transport through all-to-all, DeepEP, or HybridEP when the routing policy is unchanged. These settings should preserve the objective and effective updates, although floating-point reduction order can produce small numerical drift that still needs validation.

Treat micro batch size and gradient accumulation as execution fingerprints. Tune them only with fixed global batch size, global batch membership/order, normalization, optimizer boundaries, and token budget, and validate fresh loss sentinels for each layout. Packing, precision, forced MoE load balancing, token dropping/capacity, and router/auxiliary loss changes are never performance-only knobs.

Treat mock data, forced balancing, disabled correctness checks, and timing-only schedules as benchmark-only shortcuts. They may be appropriate in perf_recipes, but their losses and checkpoints are not convergence evidence.

For comparable model-verification recipes, choose a cohort-wide convergence contract before tuning performance. Keep the same bounded data selection, preprocessing, sequence length, global batch, optimizer/schedule, precision, seeds, routing policy, optimizer-step horizon, and processed-token checkpoints where the architectures permit. Record any necessary model-specific deviation and do not present that result as apples-to-apples convergence evidence. Absolute losses from different architectures or tokenizers are not directly rankable; compare stability and trend at equal token counts.

When a recipe’s batch disagrees with the chosen convergence contract, modify and validate the library recipe separately. A declared bounded-verification protocol may explicitly apply the same LR, schedule, sequence, and data overrides across a cohort, but do not make one-off convergence changes merely to improve throughput. Conversely, first try TP/PP/CP/EP, recompute/offload, dispatcher transport, overlap, fusion, and CUDA graphs when optimizing fit or throughput.

Entry Points#

Library recipes (functional training)#

# Pretrain with mock data
uv run python -m torch.distributed.run --nproc_per_node=8 scripts/training/run_recipe.py \
    --recipe <recipe_function_name> \
    --dataset mock

# SFT with SQuAD
uv run python -m torch.distributed.run --nproc_per_node=8 scripts/training/run_recipe.py \
    --recipe <recipe_function_name> \
    --dataset squad

# Override any field via CLI
uv run python -m torch.distributed.run --nproc_per_node=8 scripts/training/run_recipe.py \
    --recipe llama3_8b_pretrain_config \
    --dataset mock \
    'model.tensor_model_parallel_size=2' \
    'train.global_batch_size=64'

Benchmark recipes (throughput benchmarks)#

./scripts/training/train.sh \
    --nodes 2 --gpus-per-node 8 \
    --account ACCOUNT --partition PARTITION --container-image IMAGE \
    --recipe qwen3_30b_a3b_pretrain_16gpu_h100_bf16_config \
    --mode pretrain

The total GPU allocation must match the count encoded in the recipe name. The user selects the node shape, and the selected partition must provide the requested hardware. The launcher does not inject benchmark offline defaults or cluster-specific launch policy. Use --env NAME for exported offline or NCCL fabric settings and repeated --srun-arg=ARG options for srun. Configure CPU/NUMA wrappers and Slurm segment sizing through the target cluster integration, or use scripts/performance/setup_experiment.py when its compatibility policies are required. The unified launcher supports exact exported text pretraining, text SFT/PEFT, Qwen-VL pretraining, and Wan pretraining recipes and infers their forward step. Text SFT/PEFT text benchmark recipes retain the flat runner’s mock-data default; Qwen-VL and Wan retain their model-specific datasets. Exported benchmark PEFT recipes are fixed LoRA configs; use a configurable library recipe for DoRA. Trailing KEY=VALUE overrides are accepted, but an overridden benchmark recipe no longer represents its canonical benchmark configuration. Use scripts/performance/setup_experiment.py for selector-based invocation, dataset replacement, topology resizing, and specialized benchmark controls.

See the Benchmark Recipe Index for important caveats before using these for anything beyond throughput benchmarking.

Benchmark Recipe Layout#

Benchmark recipes use the same Python function format as library recipes, but live in a dedicated namespace for throughput benchmarking:

  • Benchmark recipes live in src/megatron/bridge/perf_recipes/<family>/<hardware>/<model>.py

  • Each benchmark recipe is a self-contained Python function (e.g. llama3_8b_pretrain_8gpu_h100_bf16_config())

  • Recipe names encode model, task, GPU count, hardware, precision, and optional variant

  • scripts/performance/utils/utils.py derives compatibility WorkloadBaseConfig views from the flat recipe itself

  • Shared helpers: _benchmark_common() (50 iters, timing, TE RNG), _perf_precision() (bf16 / fp8_cs / fp8_mx / nvfp4)

Why Python, not YAML? Previous YAML-based approaches had problems: recipe logic was split across multiple indirection layers, configs were not self-contained, and the two-level pipeline made maintenance and debugging difficult. Python functions are explicit, greppable, and composable.

The training launcher discovers library and benchmark recipes from the complete exported function name. Five legacy duplicate names select the benchmark definition; use the corresponding generic alias for those functional workloads. New recipe names should be unique across both packages.

Library Recipe Index#

All recipes live under src/megatron/bridge/recipes/. Each function returns a ConfigContainer with model, training, optimizer, and data settings.

Llama#

Recipe

Mode

TP

PP

CP

SP

GPUs (min)

Seq Len

llama2_7b_pretrain_config

Pretrain

2

1

—

—

2

4K

llama3_8b_pretrain_config

Pretrain

2

1

—

✓

2

8K

llama3_8b_16k_pretrain_config

Pretrain

2

1

2

✓

4

16K

llama3_8b_64k_pretrain_config

Pretrain

2

1

4

✓

8

64K

llama3_8b_128k_pretrain_config

Pretrain

2

1

8

✓

16

128K

llama3_70b_pretrain_config

Pretrain

8

4

—

✓

32

8K

llama3_70b_16k_pretrain_config

Pretrain

8

4

2

✓

64

16K

llama3_70b_64k_pretrain_config

Pretrain

8

4

4

✓

128

64K

llama31_405b_pretrain_config

Pretrain

8

16

—

✓

128

8K

llama3_8b_sft_config

SFT

2

1

—

✓

2

8K

llama3_70b_sft_config

SFT

4

4

—

✓

16

8K

llama31_405b_sft_config

SFT

8

8

—

✓

64

8K

llama3_8b_peft_config

PEFT

1

1

—

—

1

8K

llama3_70b_peft_config

PEFT

2

4

—

✓

8

8K

llama31_405b_peft_config

PEFT

4

8

—

✓

32

8K

Qwen2 / Qwen2.5#

Recipe

Mode

TP

PP

Sizes

qwen2_*_{pretrain,sft,peft}_config

All

1–8

1–4

500M, 1.5B, 7B, 14B, 32B, 72B

qwen25_*_{pretrain,sft,peft}_config

All

1–8

1–4

500M, 1.5B, 3B, 7B, 14B, 32B, 72B

Qwen3 (Dense)#

Recipe

Mode

TP

PP

CP

GPUs

Sizes / notes

qwen3_8b_pretrain_config

Pretrain

1

1

—

16

Bounded convergence cohort

qwen3_8b_sft_config

SFT

4

1

—

4

2K bounded convergence cohort

qwen3_8b_sft_32k_config

SFT

4

1

2

8

Separate 32K long-context cohort

qwen3_8b_peft_config

PEFT

1

1

—

1

Bounded LoRA/DoRA cohort

qwen3_*_{pretrain,sft,peft}_config

All

1–8

1–2

—

varies

Other dense sizes, 600M–32B

qwen3_600m_sft_128k_config

SFT

1

1

8

8

600M, 128K sequence

Qwen3 MoE#

Recipe

Mode

TP

PP

EP

CP

GPUs

qwen3_30b_a3b_pretrain_config

Pretrain

1

1

16

—

16

qwen3_30b_a3b_sft_config

SFT

1

1

16

—

16

qwen3_30b_a3b_peft_config

PEFT

4

1

4

—

4

qwen3_235b_a22b_pretrain_config

Pretrain

4

16

8

2

256

qwen3_235b_a22b_sft_config

SFT

4

16

4

—

64

qwen3_235b_a22b_peft_config

PEFT

1

4

4

—

16

Qwen3-Next#

Recipe

Mode

TP

PP

EP

qwen3_next_80b_a3b_pretrain_config

Pretrain

1

4

8

qwen3_next_80b_a3b_sft_config

SFT

1

2

8

qwen3_next_80b_a3b_peft_config

PEFT

1

1

4

DeepSeek#

Recipe

Mode

TP

PP

EP

GPUs

deepseek_v2_lite_pretrain_config

Pretrain

1

1

8

8

deepseek_v2_pretrain_config

Pretrain

1

4

32

128

deepseek_v3_pretrain_config

Pretrain

2

16

64

2048

deepseek_v3_pretrain_config_32nodes

Pretrain

2

8

32

256

GLM-4.5#

Recipe

Mode

TP

PP

EP

GPUs

glm45_355b_pretrain_config

Pretrain

2

8

16

256

glm45_air_106b_pretrain_config

Pretrain

1

4

8

32

glm45_355b_sft_config

SFT

2

8

16

256

glm45_air_106b_sft_config

SFT

1

4

8

32

glm45_355b_peft_config

PEFT

2

4

4

32

glm45_air_106b_peft_config

PEFT

1

2

4

8

Gemma#

Recipe

Mode

TP

PP

Sizes

gemma2_*_{pretrain,sft,peft}_config

All

2–8

1–2

2B, 9B, 27B

gemma3_1b_{pretrain,sft,peft}_config

All

1

1

1B (32K seq)

NemotronH / Nemotron#

Recipe

Mode

TP

PP

EP

Notes

nemotronh_{4b,8b,47b,56b}_*_config

P/S/PEFT

1–8

1–4

—

Dense SSM-hybrid

nemotron_3_nano_*_config

P/S/PEFT

varies

1

8

MoE + Mamba

nemotron_3_super_*_config

P/S/PEFT

4

1

8

MoE + Mamba, ~40% CUDA graph gain

nemotron_nano_{9b,12b}_v2_*_config

P/S/PEFT

varies

1

—

Dense

Other Models#

Recipe

Mode

Notes

moonlight_16b_pretrain_config

Pretrain

16 GPUs, TP1/PP1/EP8 with HybridEP; GBS/MBS 1024/2 bounded convergence cohort

moonlight_16b_sft_config

SFT

8 GPUs, TP1/PP1/EP8; 8K offline packing, GBS/MBS 8/1, 65,536 tokens/update

moonlight_16b_peft_config

PEFT

4 GPUs, TP1/PP1/EP4; bounded LoRA/DoRA cohort

moonlight_16b_sft_8k_config

SFT

8 GPUs, TP2/PP1/CP2/EP8; separate 8K cohort

olmoe_7b_{pretrain,sft,peft}_config

All

MoE EP=8

ministral3_{3b,8b,14b}_{sft,peft}_config

SFT/PEFT

Dense

gpt_oss_20b_*_config

All

MoE + FP8/MXFP8 variants

gpt_oss_120b_*_config

All

MoE

vanilla_gpt_pretrain_config

Pretrain

MLM/Bridge parity baseline

gpt3_175b_pretrain_config

Pretrain

TP=4, PP=8, VP=6

kimi_k2_pretrain_config

Pretrain

1T MoE, TP=2 PP=16 EP=32

VLM Recipes#

Recipe

Mode

TP

PP

EP

GPUs

gemma3_vl_{4b,12b,27b}_{sft,peft}_config

SFT/PEFT

1–8

1–2

—

1–16

qwen25_vl_{3b,7b,32b,72b}_{sft,peft}_config

SFT/PEFT

1–8

1–4

—

1–32

qwen3_vl_{8b,30b_a3b,235b_a22b}_{sft,peft}_config

SFT/PEFT

1–4

1–8

1–32

1–512

qwen35_vl_*_{sft,peft}_config

SFT/PEFT

varies

varies

varies

varies

glm_45v_{sft,peft}_config

SFT/PEFT

1

8

4–16

64–512

nemotron_nano_v2_vl_12b_{sft,peft}_config

SFT/PEFT

2–4

1

—

8

Diffusion Recipes#

Recipe

Mode

TP

CP

wan_1_3B_{pretrain,sft}_config

P/SFT

1

8

wan_14B_{pretrain,sft}_config

P/SFT

2

4

flux_12b_{pretrain,sft}_config

P/SFT

2

1

Benchmark Recipe Index#

Benchmark recipe source lives under src/megatron/bridge/perf_recipes/. The compatibility launcher in scripts/performance/ resolves those flat recipe names and derives compatibility workload views from the selected flat recipe when legacy helper paths still need them.

Important: Benchmark recipes are designed for upper-bound throughput measurements, not production training. Text benchmarks use mock data by default; Qwen-VL and Wan retain their model-specific datasets. Throughput numbers are aspirational targets, not validated convergence configs.

Llama 3 / 3.1#

Model

GPUs

GPU Types

Key Features

Llama 3 8B

8

H100, B200, B300, GB200, GB300, R100

CUDA graphs (local), FSDP on GB variants

Llama 3 70B

64

H100, B200, B300, GB200, GB300

TP comm overlap (userbuffers), FSDP, CUDA graphs

Llama 3.1 405B

128–1024

H100, B200, B300, GB200, GB300

TP+CP comm overlap (userbuffers), FSDP, heavy PP/VP

SFT/LoRA variants also exist (e.g. 8B SFT with packed sequences, 70B SFT on 32 GPUs).

DeepSeek V3#

Model

GPUs

GPU Types

Key Features

DeepSeek V3 (671B MoE)

256–1024

H100, B200, B300, GB200, GB300

HybridEP dispatcher, MLA recompute, CUDA graphs (TE scoped)

Qwen3 MoE#

Model

GPUs

GPU Types

Key Features

Qwen3 30B-A3B

8–16

H100, B200, B300, GB200, GB300

MoE alltoall/flex dispatcher

Qwen3 235B-A22B

64–256

H100, B200, B300, GB200, GB300

TP comm overlap, CUDA graphs, MoE a2a overlap

Qwen3-Next 80B-A3B

64–128

H100, B200, B300, GB200, GB300

EP 64–128

Qwen3-VL#

Model

GPUs

GPU Types

Key Features

Qwen3-VL 30B-A3B

8–16

H100, B200, B300, GB200, GB300

VLM + MoE

Qwen3-VL 235B-A22B

64–256

H100, B200, B300, GB200, GB300

VLM + MoE, TP comm overlap

Kimi K2#

Model

GPUs

GPU Types

Key Features

Kimi K2 (1T MoE)

256–1024

H100, B200, B300, GB200, GB300

Muon/Adam optimizer, HybridEP, pipeline layout helpers

NemotronH#

Model

GPUs

GPU Types

Key Features

Nemotron 3 Nano (30B MoE+Mamba)

8–16

H100, B200, B300, GB200, GB300

TE CUDA graphs (attn+mamba+moe), HybridEP

Nemotron 3 Super

64

H100, B200, B300, GB200, GB300

TE CUDA graphs, EP=64

NemotronH 56B

64

H100, B200, B300

TP=2–8, TE graphs (mamba+attn)

GPT-OSS#

Model

GPUs

GPU Types

Key Features

GPT-OSS 120B

64

H100, B200, GB200

EP=64, HybridEP on GB200

Recommendation Decision Tree#

User wants to train a model
│
├─ Know the model name?
│   ├─ Yes → Look up in Library Recipe Index above
│   │   ├─ Has a recipe for their size + mode? → Use it directly
│   │   └─ No exact match? → Use closest size, adjust parallelism
│   └─ No → Ask for model name, size, and HF model ID
│
├─ What's the training goal?
│   ├─ Pretrain → Use *_pretrain_config
│   ├─ SFT (full fine-tune) → Use *_sft_config
│   └─ PEFT (LoRA/DoRA) → Use *_peft_config (lowest GPU requirement)
│
├─ How many GPUs?
│   ├─ 1 GPU → Only PEFT recipes work (TP=1, PP=1)
│   ├─ 8 GPUs (1 node) → Most 8B–16B models, small MoE (EP=8)
│   ├─ 16–64 GPUs → 70B dense, medium MoE
│   └─ 128+ GPUs → 405B+, large MoE (DeepSeek V3, Kimi K2)
│
├─ Want throughput benchmarks?
│   ├─ Yes → Use benchmark recipes (src/megatron/bridge/perf_recipes/)
│   │   ├─ Exact exported recipe → scripts/training/train.sh --recipe <exact function name>
│   │   └─ Selector/specialized workflow → scripts/performance/setup_experiment.py
│   └─ No → Use library recipes (scripts/training/run_recipe.py)
│
└─ Long context?
    ├─ > 8K → Need CP (context parallelism), check *_16k / *_64k / *_128k variants
    └─ ≤ 8K → Default recipes work

Adjustment Advice (When Recommending)#

Parallelism Resizing Rules#

When the user’s GPU count differs from the recipe default:

  1. TP must divide num_key_value_heads (GQA constraint). E.g. if num_key_value_heads=8, valid TP = {1, 2, 4, 8}.

  2. TP should stay within a single node (NVLink). TP > 8 requires inter-node NVLink (e.g., GB200 NVL72).

  3. PP adds pipeline bubbles. Minimize PP; only increase when TP alone can’t fit the model. Use VP (virtual pipeline) to mitigate bubble overhead.

  4. EP doesn’t reduce dense-layer memory. Only expert parameters shard with EP. Shared attention/embeddings are replicated. For “OOM with MoE”, increase EP first, not TP.

  5. SP should be True whenever TP > 1. It eliminates redundant activation copies and is essentially free.

  6. CP requires all-to-all or ring attention. Check cp_comm_type. For GQA models, a2a+p2p hierarchical CP allows CP > num_kv_heads.

  7. Dense and expert meshes overlap. Do not multiply TP and EP together. The minimum MoE world size is PP × max(TP × CP, EP × ETP). Dense DP is world_size / (TP × PP × CP) and expert EDP is world_size / (PP × EP × ETP); both quotients must be integral, and the expert count must be divisible by EP.

Batch Size Tuning#

  • Start with the recipe’s micro_batch_size. If OOM, reduce to 1.

  • global_batch_size determines learning dynamics. Scale with DP: GBS = micro_batch_size × DP × gradient_accumulation_steps.

  • For MoE, micro_batch_size=1 is typical at scale.

Common Pitfalls to Warn About#

Pitfall

Symptom

Fix

TP > num_kv_heads

Crash: “TP must divide num_query_groups”

Reduce TP to a divisor of num_kv_heads

PP without VP

Poor throughput (large bubble)

Set virtual_pipeline_model_parallel_size

EP too low for large MoE

OOM on expert params

Increase EP; each expert lives on EP/num_experts ranks

CUDA graphs + packed sequences

Assert: “CUDA graph accepts only Tensor inputs”

Disable packing or use local full-iteration graphs

CUDA graphs + full recompute

Assert: “full recompute only with full iteration CUDA graph”

Disable recompute or switch to local impl

use_te_rng_tracker not set

Assert on provider init when CUDA graphs enabled

Set cfg.model.use_te_rng_tracker = True and cfg.rng.te_rng_tracker = True

FSDP + TP > 1 on H100

Possible comm bottleneck

Prefer FSDP with TP=1 or TP=2 on H100; FSDP shines on GB/B-series

Long context without CP

OOM on activations

Add CP=2/4/8; use *_16k, *_64k, or *_128k recipe variants

MoE overlap_grad_reduce on H100

May hurt throughput (False in many H100 presets)

Set overlap_grad_reduce=False for MoE on H100

VLM SFT missing image data

Runs but produces garbage

Provide actual multimodal dataset or use mock VLM data

Qwen35-VL MoE FSDP

Tested on Blackwell only

May not work on H100; validate first

Recipe Override Examples#

# Scale Llama3 8B from 2 GPUs to 8 GPUs (increase DP)
uv run python -m torch.distributed.run --nproc_per_node=8 scripts/training/run_recipe.py \
    --recipe llama3_8b_pretrain_config \
    --dataset mock

# Run the native 4-GPU Qwen3-MoE 30B PEFT topology
uv run python -m torch.distributed.run --nproc_per_node=4 scripts/training/run_recipe.py \
    --recipe qwen3_30b_a3b_peft_config \
    --dataset tulu3

# Add long context to an existing recipe
uv run python -m torch.distributed.run --nproc_per_node=8 scripts/training/run_recipe.py \
    --recipe llama3_8b_pretrain_config \
    --dataset mock \
    'model.seq_length=32768' \
    'model.context_parallel_size=4'

# Enable CUDA graphs on any recipe
uv run python -m torch.distributed.run --nproc_per_node=8 scripts/training/run_recipe.py \
    --recipe qwen3_30b_a3b_pretrain_config \
    --dataset mock \
    'model.cuda_graph_impl=transformer_engine' \
    'model.cuda_graph_scope=[attn,moe_router,moe_preprocess]' \
    'model.use_te_rng_tracker=True' \
    'rng.te_rng_tracker=True'

Quick Reference: Which Recipe for My Situation?#

I want to…

Start with

GPUs needed

Try Bridge for the first time

llama3_8b_pretrain_config + mock data

2

Fine-tune a 7-8B model

llama3_8b_sft_config or qwen3_8b_sft_config

2–4

LoRA on 1 GPU

llama3_8b_peft_config or qwen3_8b_peft_config

1

Pretrain a dense 70B

llama3_70b_pretrain_config

32–64

Train a small MoE

qwen3_30b_a3b_pretrain_config

16

Train a large MoE (235B+)

qwen3_235b_a22b_pretrain_config

256–512

Benchmark text-pretrain throughput

Benchmark recipe via train.sh --recipe <exact name>

Exact encoded count

Long-context training

llama3_8b_128k_pretrain_config or add CP override

16+

VLM fine-tuning

qwen3_vl_8b_sft_config or gemma3_vl_*_sft_config

4–8

Diffusion training

wan_1_3B_pretrain_config or flux_12b_pretrain_config

8

Code Anchors#

What

Path

Library recipes root

src/megatron/bridge/recipes/

Recipe __init__.py (all exports)

src/megatron/bridge/recipes/__init__.py

Common recipe helpers

src/megatron/bridge/recipes/common.py

Training entry point

scripts/training/run_recipe.py

Training Slurm launcher

scripts/training/train.sh

Benchmark recipes root

src/megatron/bridge/perf_recipes/

Benchmark compatibility launcher

scripts/performance/setup_experiment.py

Benchmark recipe helpers

scripts/performance/utils/utils.py

Benchmark overrides

scripts/performance/utils/overrides.py