- Replace non-ASCII symbols (checkmarks, arrows, bullets, em-dashes)
with ASCII equivalents in Rich console output to prevent
UnicodeEncodeError on Windows without PYTHONIOENCODING=utf-8
- Add _validate_output_path() for AWQ/GPTQ export — output path
traversal is now checked before import check (previously unreachable
when autoawq/auto-gptq not installed)
- 4 new tests for output path validation (2065 total, 0 failures)
- Update SECURITY.md with v0.22.0–v0.24.1 hardening history
Part A: HuggingFace Dataset browser
- soup data search: search HF Hub for datasets (sort by downloads/likes)
- soup data preview: preview remote dataset metadata, splits, features
- soup data download: stream HF dataset to local JSONL (with format conversion)
- Security: trust_remote_code=False, path traversal protection, samples cap at 1M
Part B: Freeze training (like LLaMA-Factory finetuning_type: freeze)
- freeze_layers / freeze_ratio config fields
- soup_cli/utils/freeze.py: detect layers, freeze bottom N
- Wired into SFT trainer before LoRA application
- Supports LLaMA (layers.N) and GPT-2 (h.N) naming
Part C: Loss watchdog (like Axolotl loss_watchdog_threshold)
- loss_watchdog, loss_watchdog_threshold, loss_watchdog_patience config
- Implemented in SoupTrainerCallback with patience counter
- Rich warning panel (stops Live display first), fires only once
- Wired into all 11 trainers via callback kwargs
Part D: Dataset info registry
- soup data register/unregister/registry commands
- ~/.soup/datasets.json local name→path+format mapping
- Name validation, path traversal protection, Rich markup escaping
82 new tests (2061 total), 74 test files.
- AWQ export (`soup export --format awq`) via autoawq, with --bits, --group-size, --calibration-data
- GPTQ export (`soup export --format gptq`) via auto-gptq, with calibration data support
- Sample packing (`packing: true`) for SFT/Pretrain trainers via TRL's native packing
- `soup data split` — train/val/test splitting with random and stratified strategies
- Curriculum learning (`curriculum: true`) — sort dataset by difficulty for staged training
- New utility: soup_cli/utils/curriculum.py (sort_by_length, create_buckets)
- Security: calibration data path traversal protection, bits validation (4/8 only)
- 1970 tests across 70 test files
The v0.22.0 release broke CI on Python 3.9 because serve.py used
PEP 604 union syntax (str | None) at module level, which requires 3.10+.
Fixed in previous commit; this bumps version to v0.22.1 for a clean PyPI release.
- fix: replace Unicode ⚠ (U+26A0) with ASCII [yellow]![/] in migrate
warnings to prevent UnicodeEncodeError on Windows cp1251/cp866
- fix: load_config() now accepts str in addition to Path
- fix: recipe count in docs corrected from 30 to 29
- chore: bump version to v0.21.1
Bugfix release: ANSI-safe CI test assertions (macOS fix), path
confinement hardening, circular import fix, rate limiting implementation,
trust_remote_code warning, new terracotta logo + Web UI color scheme.
Show GitHub URL in `soup version`, `soup version --full`, `soup doctor`,
and `soup --help` so users can find and star the repo.
Extract URL to GITHUB_URL constant in utils/constants.py.
- Check stdout encoding instead of type to detect non-UTF-8 consoles
- Redirect stdout to UTF-8 TextIOWrapper before plotext renders
- Add unit test that simulates cp1251 stdout with plotext
- 1022 tests total
- soup data validate: default --format changed from 'alpaca' to 'auto',
uses detect_format() to auto-detect dataset format
- soup data stats: force UTF-8 stdout on Windows for plotext histograms
- soup ui: add --show-token flag, document auth token in --help
- 7 new tests (BUG-013/014/015), 1021 tests total
Post-release fixes from code review agents:
- Guard _output_dir in PretrainTrainerWrapper.train()
- 12 new tests: MoE integration, DeepSeek naming, model types, Unicode
- 1014 total tests, 58.31% coverage
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Add continued pre-training task and Mixture of Experts model support:
- `task: pretrain` for continued pre-training on raw text data
- `plaintext` data format ({"text": "..."} JSONL or .txt files)
- MoE model detection (Mixtral, Qwen3 MoE, DeepSeek V3, DBRX, OLMoE)
- ScatterMoE LoRA (`moe_lora: true`) targets expert FFN + attention layers
- `moe_aux_loss_coeff` for router load-balancing loss
- Templates: `soup init --template pretrain` and `--template moe`
- 85 new tests across test_pretrain.py and test_moe.py (1002 total)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Test _load_model exit paths: adapter without base model, corrupt JSON
- Test _generate branches: greedy (temp=0), sampling (temp>0), no
chat_template fallback, token count from tensor shape, role formatting
- Test max_tokens bounds: 0 and 99999 rejected by CLI
- Test tensorboard happy path: flag accepted when tensorboard installed
- Fix import-failure test: avoid builtins.__import__ recursion
- 917 tests, 44 test files, 57.92% coverage
- Fix test_tensorboard_in_train_help: strip ANSI escape codes before
asserting --tensorboard in help output (Rich splits flag across
escape sequences on Python 3.11)
- Fix TensorBoard import check: use `import tensorboard` directly
- Stream JSONL output during inference (crash-safe for large files)
- Return accurate token count from _generate via tensor shape
- Replace shallow tests with real trainer integration tests
- Cap max_tokens at 16384 + trust_remote_code warning
Security fixes across all HTTP surfaces:
- Web UI: Bearer token auth on mutating endpoints, CORS restricted to served origin,
path traversal protection on /api/data/inspect, config validated before training,
removed user-controlled config_path from API
- Serve/vLLM: max_tokens capped at 16384, generic error messages (no stack traces)
- Generate: SSRF protection (--api-base blocks non-HTTPS for remote URLs),
--api-key deprecated in favor of OPENAI_API_KEY env var
- Export: llama.cpp pinned to tag b5270 (supply-chain safety)
- Push: --token deprecated in favor of HF_TOKEN env var
- Rewards: warning before executing custom .py reward files
- Tests: all 40 UI tests updated with auth headers, 666 tests pass
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Updated color scheme from warm brown/orange to purple/cyan palette:
- Dark navy backgrounds (#0c0e14, #14171f)
- Purple accent (#8b6cc7) for primary actions
- Cyan accent (#5ce0d8) for highlights and stats
- Updated Chart.js colors and all UI components
- New logo in UI static assets
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
GRPO:
- Set default chat_template on tokenizer when missing (fixes ValueError
from trl's apply_chat_template on models without chat support)
- Ensure batch_size >= num_generations (trl 0.28 requirement)
- Verified end-to-end GRPO training on CPU succeeds
PPO:
- Tokenize dataset via .map() before passing to PPOTrainer (adds input_ids
and attention_mask columns required by trl experimental API)
Tests: 666 passed, 5 new tests for chat_template/tokenization fixes,
3 existing mock tests updated for new tokenization step.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- PPO: Skip resume_from_checkpoint when experimental PPOTrainer.train()
doesn't accept it (inspect signature at runtime, warn and proceed).
- CPU: Use device_map="cpu" instead of "auto" on CPU across all trainers
(SFT, DPO, GRPO, PPO, RewardModel) to prevent meta tensor errors.
- Add 12 new tests for both fixes (661 total passing).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- PPO: Support trl >=0.28 experimental API (ref_model, reward_model,
train_dataset, value_model positional args). Auto-import from
trl.experimental.ppo with fallback. Create reward/value models when needed.
- GRPO: Fix CPU empty generation tensor mismatch by passing
generation_kwargs={"min_new_tokens": 1} on CPU devices.
- Add 6 new tests for both fixes (649 total passing).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
PPOTrainer.__init__() no longer accepts dataset= in newer trl versions.
Now checks via inspect.signature whether train_dataset or dataset is
accepted; if neither, sets dataset on trainer before .train() call.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- PPO: add use_cpu=True to PPOConfig when running on CPU
- GRPO: add CPU warning + use_cpu flag via inspect (trl bug workaround)
- Add use_cpu error pattern to friendly error map
- 7 new tests for CPU fixes (637 total, all passing)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- ASCII progress bar in display.py (replace Unicode blocks with #/-)
- Catch UnicodeEncodeError from plotext in data stats (Windows fallback)
- Auto-disable 4bit/8bit quantization on CPU (bitsandbytes doesn't support it)
- Friendly error messages for CPU tensor/dtype/bf16/torchvision issues
- Fix diff.py: torch_dtype= -> dtype= (deprecation warning)
- Add torchvision version compatibility check to soup doctor
7 new tests (631 total), ruff clean.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- BUG-001: Replace Unicode arrows/dashes with ASCII in all console output
to fix UnicodeEncodeError on Windows cp1252 (~10 commands affected)
- BUG-002: PPO trainer uses inspect.signature to detect trl parameter names
(ppo_epochs vs num_ppo_epochs) for trl 0.28.0 compatibility
- BUG-003: Add get_compute_dtype() - uses float32 on CPU, bfloat16/float16
on CUDA. Fixes dtype mismatch in reward model and all trainers
- BUG-004: Add warning when using quantization on CPU
- BUG-005: Fix dtype -> torch_dtype in diff.py model loading
- BUG-006: Pin wandb<0.18.0 to avoid trl import conflict, add runtime guard
13 new tests (624 total), ruff clean.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Add --backend vllm flag to soup serve for 2-4x better inference throughput
- Add --tensor-parallel and --gpu-memory flags for vLLM tuning
- Auto-detect vLLM and show hint when installed but not enabled
- New utils/vllm.py with engine creation, app factory, LoRA support
- Native token-by-token streaming via vLLM AsyncLLMEngine
- Add serve-fast extra: pip install 'soup-cli[serve-fast]'
- Add vllm detection to version --full
- 30 new tests (560 total), ruff clean
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The force-include config duplicated static files already included via
packages = ["soup_cli"], causing PyPI to reject the wheel with 400.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- `soup ui` command launches local web interface at http://127.0.0.1:7860
- FastAPI backend with REST API: runs, metrics, config validation, training
control, data inspection, templates, system info
- Self-contained SPA frontend (Dashboard, New Training, Data Explorer,
Model Chat) with Chart.js loss/LR charts
- Auto-opens browser on launch (--no-browser to disable)
- Config validation via new load_config_from_string() in config/loader.py
- 40 new tests (530 total), ruff clean
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Add `modality: vision` config option for vision-language model training
- Add LLaVA and ShareGPT4V data format detection and conversion
- Add `image_dir` field in DataConfig for resolving image paths
- Add vision model loading via AutoModelForVision2Seq + AutoProcessor in SFT trainer
- Add `soup init --template vision` with LLaMA-3.2-Vision config
- Add image statistics display in `soup data inspect` for vision datasets
- Add Pillow as optional `vision` extra dependency
- Add Pillow to `soup doctor` dependency checks
- 51 new tests (455 total), ruff clean
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Alpamys