mirror of https://github.com/razor-ai/soup.git
e4c3042a56
Ships v0.25.0 with eight new capabilities (Parts A–H) that close every competitive gap vs LLaMA-Factory/Axolotl/Unsloth and add unique differentiators: Part A — 9 new model recipes: Llama 4 Scout (sft/dpo/grpo), Qwen 3 14B/32B/8B-grpo, Gemma 3 12B/27B-dpo, DeepSeek V3 (MoE LoRA). Part B — Tool-calling / agentic fine-tuning: new "tool-calling" data format with detection + normalization, synth data template, init template, eval scoring (tool_call_match / tool_call_name_match / tool_call_args_subset), plus qwen3-8b-tools and llama4-scout-tools recipes. Part C — RLVR (RL from Verifiable Rewards): reward_fn=verifiable routing to math_verify_reward (regex-only, no eval), code_exec_reward (subprocess sandbox with RLIMIT_AS/RLIMIT_CPU on POSIX, ephemeral tempdir cwd, concurrency cap, one-time warning panel), and json_schema_reward. verifiable_domain Literal validated via model_validator. Part D — VeRA + OLoRA PEFT methods: LoraConfig.use_vera / use_olora with mutual-exclusion validator and a unified peft_builder helper that returns either LoraConfig or VeraConfig with the right init kwargs. Part E — Apple Silicon MLX backend: detection + hardware profiling in utils/mlx, MLXSFTTrainerWrapper via mlx-lm, scaffolding DPO/GRPO wrappers rejected at config load time by SoupConfig._validate_mlx_task_support, lazy trainer registry, doctor integration, 3 MLX SFT recipes, [mlx] extra in pyproject. Part F — Data augmentation: soup data augment with rephrase / translate / style strategies, path-traversal-protected input/output, count capped 1-10, lang/styles lists bounded (10 entries × 32 chars), rate limiting, and optional --dedup. Part G — Training intelligence: forgetting detection (ForgettingDetector with 3 built-in mini benchmarks and warning levels) and checkpoint intelligence (CheckpointTracker with composite metric, early-stop on regression, safe top-N pruning refusing symlinks and non-checkpoint dirs). SQLite schema extended with checkpoint_quality + forgetting_eval tables. Part H — Autopilot: soup autopilot command with dataset/model/hardware profilers, decision engine (task/quant/peft/batch/lr/epochs/max_length/perf flags), YAML generator, and full CLI with dry-run + --yes + path-traversal protection + goal whitelist + gpu_budget bounds [1GB, 1TB]. Bakes forgetting detection + checkpoint intelligence + early-stop into the generated config. Totals: - 2313 tests passing (183 new, up from 2130) - 86 test files (8 new) - 43 ready-made recipes (14 new) - 16 built-in templates (tool-calling added) - Review findings: all CRITICAL/HIGH/MEDIUM/LOW addressed (3 documented design limitations: code_exec best-effort sandbox, prune_checkpoints TOCTOU, MLX training integration test requires real hardware) Docs: CLAUDE.md, README.md, SECURITY.md, CONTRIBUTING.md updated. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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| .. | ||
| __init__.py | ||
| constants.py | ||
| curriculum.py | ||
| deepspeed.py | ||
| errors.py | ||
| flash_attn.py | ||
| freeze.py | ||
| fsdp.py | ||
| galore.py | ||
| gpu.py | ||
| liger.py | ||
| long_context.py | ||
| mlx.py | ||
| moe.py | ||
| ollama.py | ||
| peft_builder.py | ||
| profiler.py | ||
| qat.py | ||
| quality.py | ||
| registry.py | ||
| ring_attention.py | ||
| sglang.py | ||
| unsloth.py | ||
| vllm.py | ||