Expand the recipe catalog from 46 to 80 entries — every popular open-weight
model family now has a validated Soup recipe.
Part A — Vision (6 recipes): Llama-3.2-Vision-90B, Pixtral-12B, Qwen2-VL
(7B + 72B), InternVL 2.5, MiniCPM-V 2.6
Part B — Audio (3 recipes): Qwen2-Audio, SeamlessM4T v2, Whisper-large-v3
Part C — Reasoning (7 recipes): completes the 6 DeepSeek-R1-Distill sizes,
plus Qwen3-Coder, Qwen3-30B-A3B reasoning, Phi-4 reasoning
Part D — Edge (8 recipes): SmolLM2 (135M / 360M / 1.7B), Qwen2.5
(0.5B / 1.5B / 3B), Gemma 2 2B, Phi-3.5-mini
Part E — Domain (8 recipes): BioMistral, Meditron, CodeLlama (13B / 70B),
Magicoder, Mathstral, Nemotron-4 340B, Llama-2-13b-finance
Part F — Multimodal reasoning (2 recipes): Llama-3.2-Vision GRPO, Pixtral DPO
Part G — Recipe-validation CI workflow on every PR touching recipe / config /
data code (.github/workflows/recipe-validation.yml)
Part H — 750 parametrized tests covering catalog-wide invariants:
model-id safety (no `..`/`://`/null bytes), lora.target_modules non-empty,
max_length within schema bounds, GRPO recipes wire reward_fn +
num_generations >= 2, vision recipes set image_dir, audio recipes set
audio_dir, default data path is non-empty + relative
Live 100-step per-recipe smoke train (requires GPU runner) deferred to v0.31.1.
Tests: 2886 → 3607 (+721). Catalog: 46 → 80 (target met).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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>
Salil M
Alpamys