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Documentation Index

Fetch the complete documentation index at: https://www.radixark.com/llms.txt

Use this file to discover all available pages before exploring further.

These features live in the Miles tree but are not production-ready. They typically have rough edges, missing parallelism, or known bugs against current dependency versions. Use them when you want to iterate quickly or co-develop a feature, not for the long-running training jobs you’d publish results from.

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FSDP backend

A PyTorch FSDP2 training backend lives at miles/backends/fsdp_utils/. It trades maximum throughput for zero conversion overhead: there is no torch_dist step, Miles reads architecture information from the HuggingFace config.json, and weights load directly via AutoModelForCausalLM.from_pretrained(). The distributed optimizer is built into FSDP, and mixed precision falls out of standard PyTorch.
Status. Experimental. Known bug after SGLang v0.5.10. No TP / PP / CP / EP — runs as plain FSDP data parallel only. Suitable for fast iteration on small-to-mid dense models, not for production runs.

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When to reach for it

  • Iterating on a new model architecture and you don’t want to write a Megatron spec yet.
  • Small-to-mid dense workloads where the parallelism story doesn’t matter.
  • You want a HuggingFace-native checkpoint at every step with no conversion.
For large MoE models, multi-rack jobs, or anything where TP / PP / CP / EP matters, use the production Megatron-LM backend instead.

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Enabling it

--train-backend fsdp

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Flag mapping vs. Megatron

Most RL-level flags carry over unchanged. Backend-specific differences:
ConcernMegatronFSDP
Model load--load + architecture args--hf-checkpoint (single flag, required)
Tensor parallel--tensor-model-parallel-sizeNot supported yet
Pipeline parallel--pipeline-model-parallel-sizeNot supported yet
Expert parallel--expert-model-parallel-sizeNot supported yet
Context parallel--context-parallel-sizeNot supported yet
Optimizer--use-distributed-optimizer (forced on by Miles)Built-in
Gradient checkpoint--recompute-granularity / method / num-layers--gradient-checkpointing (boolean)
CPU offloadDistributed optimizer--fsdp-cpu-offload
CPU backend(in distributed optimizer)--fsdp-cpu-backend
Attention backendDecided by Megatron Core--attn-implementation flash_attention_2 / sdpa / eager
Mixed precision--fp16 / --bf16--fp16 (bf16 inferred)
Extra backend config—--config <yaml>

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Quick start

export WANDB_API_KEY=<key>

# Model + data
hf download Qwen/Qwen3-4B                                       --local-dir /root/Qwen3-4B
hf download --repo-type dataset BytedTsinghua-SIA/DAPO-Math-17K --local-dir /root/dapo-math-17k
hf download --repo-type dataset zhuzilin/aime-2024              --local-dir /root/aime-2024

# Code
git clone https://github.com/radixark/miles.git && cd miles
pip install -e . --no-deps

# Launch — no conversion step
bash scripts/run-qwen3-4B-fsdp.sh