mirror of https://github.com/razor-ai/soup.git
feat(recipes): 2026 model-family expansion — 17 SFT recipes, catalog 116→133 (v0.71.24)
Add ready-made SFT recipes for the open-weight models released Feb–Jun 2026, each base repo-ID verified to resolve on Hugging Face: - Qwen3.5 0.8B/2B/4B/9B/27B + MoE 35B-A3B/122B-A10B/397B-A17B (Apache-2.0) - Qwen3.6 27B + 35B-A3B (Apache-2.0) - DeepSeek-V4 Flash/Pro (MIT), GLM-5.1 (MIT) - Kimi-K2.5/K2.6 (Modified MIT), MiniMax-M3 (MiniMax Community License) - Mistral-Large-3 (Apache-2.0, 675B/41B-active multimodal MoE) Fix stale glm-5-sft repo-ID THUDM/glm-5 -> zai-org/GLM-5 (org migration). +220 tests (tests/test_v07124.py). Catalog count 116 -> 133.
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CHANGELOG.md
21
CHANGELOG.md
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@ -12,10 +12,31 @@ reproducing 70+ versions of notes.
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## [Unreleased]
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## [0.71.24] - 2026-06-21
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### Added
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- **2026 model-family recipe expansion (catalog 116 → 133).** 17 new ready-made
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SFT recipes for the open-weight models released Feb–Jun 2026, each with its
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Hugging Face repo-ID verified to resolve:
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- **Qwen 3.5 (Apache-2.0):** `qwen3.5-0.8b-sft`, `qwen3.5-2b-sft`,
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`qwen3.5-4b-sft`, `qwen3.5-9b-sft`, `qwen3.5-27b-sft`, and the
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`qwen3.5-35b-a3b-sft` / `qwen3.5-122b-a10b-sft` / `qwen3.5-397b-a17b-sft`
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MoE sizes.
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- **Qwen 3.6 (Apache-2.0):** `qwen3.6-27b-sft`, `qwen3.6-35b-a3b-sft`.
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- **DeepSeek-V4 (MIT):** `deepseek-v4-flash-sft`, `deepseek-v4-pro-sft`.
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- **GLM (MIT):** `glm-5.1-sft`.
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- **Kimi (Modified MIT):** `kimi-k2.5-sft`, `kimi-k2.6-sft`.
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- **MiniMax (MiniMax Community License — commercial use needs a separate
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agreement):** `minimax-m3-sft`.
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- **Mistral Large 3 (Apache-2.0, 675B/41B-active multimodal MoE):**
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`mistral-large-3-sft`.
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- Unit-test coverage for the `warmup.py` auto-warmup-steps helper
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([#274](https://github.com/MakazhanAlpamys/Soup/pull/274) by [@shatakshi-1404](https://github.com/shatakshi-1404)).
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### Fixed
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- **Stale recipe repo-ID:** `glm-5-sft` now points at `zai-org/GLM-5` (the org
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migrated from `THUDM`).
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## [0.71.23] - 2026-06-12
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### Added
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@ -110,7 +110,7 @@ src/soup_cli/
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experiment/ - SQLite experiment tracking
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eval/ - Eval platform (custom tasks, LLM judge, human eval, leaderboard)
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migrate/ - Config migration (LLaMA-Factory, Axolotl, Unsloth)
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recipes/ - Ready-made configs for popular models (116 recipes)
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recipes/ - Ready-made configs for popular models (133 recipes)
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autopilot/ - Zero-config decision engine (v0.25.0)
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registry/ - Model Registry (hashing, store, diff, attach) (v0.26.0 + v0.33.0)
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cans/ - Shareable .can artifact format + run/publish orchestrator (v0.26.0 + v0.33.0)
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@ -120,7 +120,7 @@ src/soup_cli/
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templates/ - 17 built-in soup.yaml templates (YAML + manifest.json) with load_template loader (v0.39.0, +bco v0.40.0)
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ui/ - Web UI (FastAPI + HTML/JS SPA)
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tests/ - Test suite (294 files, 14278 tests)
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tests/ - Test suite (296 files, 14514 tests)
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examples/ - Real-world config examples and datasets
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```
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@ -150,7 +150,7 @@ pytest tests/test_data.py::test_detect_alpaca_format -v
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pytest tests/ --cov=soup_cli --cov-report=html
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```
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### Test Files (293 files)
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### Test Files (296 files)
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> A representative sample of the suite below. The full table lives in
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> [`.claude/CLAUDE.md`](.claude/CLAUDE.md); run `pytest tests/ -v` for the complete list.
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22
README.md
22
README.md
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@ -49,19 +49,17 @@ infrastructure instead of improving models. Soup fixes that.
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## What's New
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**v0.71.23 — Native Spectrum targeted training.** Fine-tune only the layers that matter — no 8×H100 needed to find them:
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**v0.71.24 — 2026 model-family recipes (catalog 116 → 133).** 17 new ready-made SFT recipes for the open-weight models that shipped Feb–Jun 2026:
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- **`soup spectrum scan --model <id|path> --top-percent 50`** — streams a model's weights one
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tensor at a time (no model load, runs on a CPU box even for very large models) and computes a
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singular-value signal-to-noise ratio per weight matrix (Marchenko-Pastur, arXiv:2406.06623).
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- **Ready-to-paste patch** — prints a per-group SNR table and a `training.unfrozen_parameters`
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YAML block; pipe it straight into your `soup.yaml`. Results cache under `~/.soup/spectrum/`.
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- **Targeted full fine-tuning** — with `training.unfrozen_parameters` set, `soup train` freezes
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every parameter and unfreezes only the matched high-SNR layers (full FT, LoRA off) — train
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fewer parameters, keep more of the base model intact.
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- **Honest guards** — the scan is pure-numpy and transpose-invariant; patterns are ReDoS-checked,
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Hub downloads go through the SSRF-hardened loader, and the config gates `quantization: none` +
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`task: sft` so a mis-set flag fails loudly, not silently.
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- **Qwen 3.5 family (Apache-2.0)** — `qwen3.5-0.8b/2b/4b/9b/27b-sft` dense, plus the
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`35b-a3b` / `122b-a10b` / `397b-a17b` MoE sizes (262K context, native vision).
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- **Qwen 3.6 (Apache-2.0)** — `qwen3.6-27b-sft` + `qwen3.6-35b-a3b-sft`.
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- **Frontier MoE** — `deepseek-v4-flash-sft` / `deepseek-v4-pro-sft` (MIT),
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`glm-5.1-sft` (MIT), `kimi-k2.5-sft` / `kimi-k2.6-sft` (Modified MIT),
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`minimax-m3-sft` (MiniMax Community License), `mistral-large-3-sft` (Apache-2.0).
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- **Stale repo-ID fix** — `glm-5-sft` now points at `zai-org/GLM-5` (the org migrated from `THUDM`).
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- Every base repo-ID was verified to resolve on Hugging Face. Grab one with
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`soup recipes use <name>` or browse all 133 via `soup recipes list`.
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Full history: [CHANGELOG.md](CHANGELOG.md) · [GitHub Releases](https://github.com/MakazhanAlpamys/Soup/releases).
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@ -129,7 +129,7 @@ soup migrate --from llamafactory config.yaml Import config from LLaMA-Factory
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soup migrate --from axolotl config.yml Import config from Axolotl
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soup migrate --from unsloth notebook.ipynb Import config from Unsloth notebook
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soup migrate --from llamafactory c.yaml --dry-run Preview without writing
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soup recipes list List all 116 ready-made recipes
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soup recipes list List all 133 ready-made recipes
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soup recipes show llama3.1-8b-sft Print recipe YAML
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soup recipes use llama3.1-8b-sft Copy recipe to soup.yaml
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soup recipes search "reasoning" Search by keyword/task/size
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@ -16,11 +16,15 @@ Soup works with **any** of the **340,000+** text-generation models on [HuggingFa
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| **Llama 3.x** | Llama-3.1-8B-Instruct, Llama-3.3-70B-Instruct | 1B–70B | Chat, instruction following |
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| **Llama 3.2 Vision** | Llama-3.2-11B-Vision-Instruct, Llama-3.2-90B-Vision | 11B–90B | Image understanding |
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| **Gemma 3** | Gemma-3-4B-IT, Gemma-3-9B-IT, Gemma-3-27B-IT | 4B–27B | Efficient, multilingual |
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| **Qwen 3.5 / 3.6** | Qwen3.5-0.8B…397B-A17B, Qwen3.6-27B, Qwen3.6-35B-A3B | 0.8B–397B | 262K context, native vision, MoE |
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| **Qwen 3** | Qwen3-8B, Qwen3-14B, Qwen3-32B, Qwen3-235B-A22B | 0.6B–235B | Reasoning, code, MoE |
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| **Qwen 2.5** | Qwen2.5-7B-Instruct, Qwen2.5-Coder-32B-Instruct | 0.5B–72B | Code, math |
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| **DeepSeek** | DeepSeek-R1-Distill-Llama-8B, DeepSeek-V3-0324 | 1.5B–671B | Reasoning (GRPO), code |
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| **DeepSeek** | DeepSeek-R1-Distill-Llama-8B, DeepSeek-V3-0324, DeepSeek-V4-Flash/Pro | 1.5B–1.6T | Reasoning (GRPO), code, MoE |
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| **GLM** | GLM-5, GLM-5.1 | 9B–754B | Chinese + English, MoE |
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| **Kimi** | Kimi-K2, Kimi-K2.5, Kimi-K2.6 | ~1T (MoE) | Long-context agentic, MoE |
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| **MiniMax** | MiniMax-M2, MiniMax-M3 | 230B–428B | Agentic, MoE (community license) |
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| **Phi-4** | Phi-4-14B, Phi-4-mini-reasoning | 3.8B–14B | Compact reasoning |
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| **Mistral** | Mistral-7B-Instruct-v0.3, Mistral-Small-24B-Instruct | 7B–24B | Fast, efficient |
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| **Mistral** | Mistral-7B-Instruct-v0.3, Mistral-Small-24B, Mistral-Large-3 | 7B–675B | Fast, efficient, MoE |
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| **Mixtral** | Mixtral-8x7B-Instruct-v0.1, Mixtral-8x22B | 47B–141B | MoE architecture |
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| **CodeLlama** | CodeLlama-7b-Instruct-hf, CodeLlama-34b-Instruct | 7B–34B | Code generation |
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| **StarCoder 2** | StarCoder2-15B, StarCoder2-7B | 3B–15B | Code completion |
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@ -418,7 +418,7 @@ soup ui
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**Pages:**
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- **Dashboard** — view all experiment runs, loss charts, system info, multi-run comparison
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- **New Training** — create configs from templates or 116 ready-made recipes, validate, start training with live SSE log streaming and progress bar
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- **New Training** — create configs from templates or 133 ready-made recipes, validate, start training with live SSE log streaming and progress bar
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- **Data Explorer** — browse and inspect datasets (JSONL, JSON, CSV, Parquet)
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- **Model Chat** — chat with streaming responses, configurable temperature/top_p/max_tokens, system prompt, adapter selection, markdown rendering, chat export
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@ -427,7 +427,7 @@ soup ui
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- **Enhanced Metrics** — 2x2 chart grid (loss, LR, grad_norm, throughput) + GPU memory chart, eval results table
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- **Multi-Run Compare** — overlay loss curves from up to 5 runs side-by-side
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- **Chat Upgrade** — SSE streaming via proxy, typing indicator, cancel button, markdown renderer (bold, italic, code blocks), chat export as JSON
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- **Config Builder** — recipe dropdown (116 recipes), config schema API for dynamic form generation
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- **Config Builder** — recipe dropdown (133 recipes), config schema API for dynamic form generation
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**Security:** The Web UI generates a random auth token at startup (printed to console). All mutating endpoints (start/stop training, delete runs, inspect data, validate config) require `Authorization: Bearer <token>` header. CORS is restricted to the served origin. Data inspection is sandboxed to the working directory.
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@ -4,7 +4,7 @@ build-backend = "hatchling.build"
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[project]
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name = "soup-cli"
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version = "0.71.23"
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version = "0.71.24"
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description = "Fine-tune and post-train LLMs in one command. No SSH, no config hell."
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readme = "README.md"
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license = "Apache-2.0"
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@ -1,3 +1,3 @@
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"""Soup CLI — Fine-tune and post-train LLMs in one command."""
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__version__ = "0.71.23"
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__version__ = "0.71.24"
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@ -48,7 +48,7 @@ def search_recipes(
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# ---------------------------------------------------------------------------
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# Recipe catalog (116 recipes)
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# Recipe catalog (133 recipes)
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# ---------------------------------------------------------------------------
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RECIPES: Dict[str, RecipeMeta] = {
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@ -2594,13 +2594,13 @@ output: ./output
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""",
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),
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"glm-5-sft": RecipeMeta(
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model="THUDM/glm-5",
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model="zai-org/GLM-5",
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task="sft",
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size="9B",
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tags=("glm", "thudm", "chat", "next-gen"),
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tags=("glm", "zai-org", "chat", "next-gen"),
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description="GLM 5 SFT (next-gen GLM family)",
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yaml_str="""\
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base: THUDM/glm-5
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base: zai-org/GLM-5
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task: sft
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data:
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@ -3536,6 +3536,553 @@ training:
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alpha: 32
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target_modules: auto
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output: ./output
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""",
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),
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# ------------------------------------------------------------------
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# v0.71.24 — 2026 model-family expansion (catalog 116 -> 133)
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# 17 SFT recipes for the open-weight models released Feb-Jun 2026.
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# Every base repo-ID verified to resolve on Hugging Face.
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# ------------------------------------------------------------------
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"qwen3.5-0.8b-sft": RecipeMeta(
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model="Qwen/Qwen3.5-0.8B",
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task="sft",
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size="0.8B",
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tags=("qwen", "qwen3.5", "sft", "tiny", "edge", "mobile"),
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description="Qwen 3.5 0.8B SFT (Apache-2.0, tiny / mobile)",
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yaml_str="""\
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base: Qwen/Qwen3.5-0.8B
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task: sft
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data:
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train: ./data/train.jsonl
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format: auto
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max_length: 2048
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training:
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epochs: 3
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lr: 3e-4
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batch_size: auto
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lora:
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r: 8
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alpha: 16
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target_modules: auto
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quantization: 4bit
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output: ./output
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""",
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),
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"qwen3.5-2b-sft": RecipeMeta(
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model="Qwen/Qwen3.5-2B",
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task="sft",
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size="2B",
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tags=("qwen", "qwen3.5", "sft", "small", "edge"),
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description="Qwen 3.5 2B SFT (Apache-2.0, small / edge)",
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yaml_str="""\
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base: Qwen/Qwen3.5-2B
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task: sft
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data:
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train: ./data/train.jsonl
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format: auto
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max_length: 2048
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training:
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epochs: 3
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lr: 2e-4
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batch_size: auto
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lora:
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r: 8
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alpha: 16
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target_modules: auto
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quantization: 4bit
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output: ./output
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""",
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),
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"qwen3.5-4b-sft": RecipeMeta(
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model="Qwen/Qwen3.5-4B",
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task="sft",
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size="4B",
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tags=("qwen", "qwen3.5", "sft", "small"),
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description="Qwen 3.5 4B SFT (Apache-2.0, 262K context)",
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yaml_str="""\
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base: Qwen/Qwen3.5-4B
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task: sft
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data:
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train: ./data/train.jsonl
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format: auto
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max_length: 4096
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training:
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epochs: 3
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lr: 2e-4
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batch_size: auto
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lora:
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r: 16
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alpha: 32
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target_modules: auto
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quantization: 4bit
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output: ./output
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""",
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),
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"qwen3.5-9b-sft": RecipeMeta(
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model="Qwen/Qwen3.5-9B",
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task="sft",
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size="9B",
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tags=("qwen", "qwen3.5", "sft", "chat", "instruction"),
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description="Qwen 3.5 9B SFT (Apache-2.0, 262K context)",
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yaml_str="""\
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base: Qwen/Qwen3.5-9B
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task: sft
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data:
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train: ./data/train.jsonl
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format: auto
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max_length: 4096
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training:
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epochs: 3
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lr: 2e-4
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batch_size: auto
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lora:
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r: 16
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alpha: 32
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target_modules: auto
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quantization: 4bit
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output: ./output
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""",
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),
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"qwen3.5-27b-sft": RecipeMeta(
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model="Qwen/Qwen3.5-27B",
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task="sft",
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size="27B",
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tags=("qwen", "qwen3.5", "sft", "large", "deepspeed"),
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description="Qwen 3.5 27B SFT (Apache-2.0) with DeepSpeed ZeRO-2",
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yaml_str="""\
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base: Qwen/Qwen3.5-27B
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task: sft
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data:
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train: ./data/train.jsonl
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format: auto
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max_length: 4096
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training:
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epochs: 3
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lr: 1e-5
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batch_size: auto
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gradient_accumulation_steps: 8
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lora:
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r: 16
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alpha: 32
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target_modules: auto
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quantization: 4bit
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output: ./output
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""",
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),
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"qwen3.5-35b-a3b-sft": RecipeMeta(
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model="Qwen/Qwen3.5-35B-A3B",
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task="sft",
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size="35B",
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tags=("qwen", "qwen3.5", "sft", "moe", "mixture-of-experts"),
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description="Qwen 3.5 35B-A3B MoE SFT (Apache-2.0, 3B active)",
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yaml_str="""\
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base: Qwen/Qwen3.5-35B-A3B
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task: sft
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data:
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train: ./data/train.jsonl
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format: auto
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max_length: 4096
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training:
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epochs: 3
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lr: 1e-4
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batch_size: auto
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gradient_accumulation_steps: 8
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lora:
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r: 16
|
||||
alpha: 32
|
||||
target_modules: auto
|
||||
quantization: 4bit
|
||||
moe_lora: true
|
||||
moe_aux_loss_coeff: 0.01
|
||||
|
||||
output: ./output
|
||||
""",
|
||||
),
|
||||
"qwen3.5-122b-a10b-sft": RecipeMeta(
|
||||
model="Qwen/Qwen3.5-122B-A10B",
|
||||
task="sft",
|
||||
size="122B",
|
||||
tags=("qwen", "qwen3.5", "sft", "moe", "large", "multi-gpu"),
|
||||
description=(
|
||||
"Qwen 3.5 122B-A10B MoE SFT (Apache-2.0, 10B active). "
|
||||
"Multi-GPU recommended (8 x A100/H100 80GB)."
|
||||
),
|
||||
yaml_str="""\
|
||||
base: Qwen/Qwen3.5-122B-A10B
|
||||
task: sft
|
||||
|
||||
data:
|
||||
train: ./data/train.jsonl
|
||||
format: auto
|
||||
max_length: 4096
|
||||
|
||||
training:
|
||||
epochs: 1
|
||||
lr: 1e-5
|
||||
batch_size: 1
|
||||
gradient_accumulation_steps: 16
|
||||
lora:
|
||||
r: 32
|
||||
alpha: 64
|
||||
target_modules: auto
|
||||
quantization: 4bit
|
||||
moe_lora: true
|
||||
moe_aux_loss_coeff: 0.01
|
||||
gradient_checkpointing: true
|
||||
|
||||
output: ./output
|
||||
""",
|
||||
),
|
||||
"qwen3.5-397b-a17b-sft": RecipeMeta(
|
||||
model="Qwen/Qwen3.5-397B-A17B",
|
||||
task="sft",
|
||||
size="397B",
|
||||
tags=("qwen", "qwen3.5", "sft", "moe", "large", "multi-gpu"),
|
||||
description=(
|
||||
"Qwen 3.5 397B-A17B flagship MoE SFT (Apache-2.0, 17B active). "
|
||||
"Requires multi-node DeepSpeed / FSDP."
|
||||
),
|
||||
yaml_str="""\
|
||||
base: Qwen/Qwen3.5-397B-A17B
|
||||
task: sft
|
||||
|
||||
data:
|
||||
train: ./data/train.jsonl
|
||||
format: auto
|
||||
max_length: 4096
|
||||
|
||||
training:
|
||||
epochs: 1
|
||||
lr: 5e-6
|
||||
batch_size: 1
|
||||
gradient_accumulation_steps: 32
|
||||
lora:
|
||||
r: 32
|
||||
alpha: 64
|
||||
target_modules: auto
|
||||
quantization: 4bit
|
||||
moe_lora: true
|
||||
moe_aux_loss_coeff: 0.01
|
||||
gradient_checkpointing: true
|
||||
|
||||
output: ./output
|
||||
""",
|
||||
),
|
||||
"qwen3.6-27b-sft": RecipeMeta(
|
||||
model="Qwen/Qwen3.6-27B",
|
||||
task="sft",
|
||||
size="27B",
|
||||
tags=("qwen", "qwen3.6", "sft", "large", "deepspeed"),
|
||||
description="Qwen 3.6 27B SFT (Apache-2.0) with DeepSpeed ZeRO-2",
|
||||
yaml_str="""\
|
||||
base: Qwen/Qwen3.6-27B
|
||||
task: sft
|
||||
|
||||
data:
|
||||
train: ./data/train.jsonl
|
||||
format: auto
|
||||
max_length: 4096
|
||||
|
||||
training:
|
||||
epochs: 3
|
||||
lr: 1e-5
|
||||
batch_size: auto
|
||||
gradient_accumulation_steps: 8
|
||||
lora:
|
||||
r: 16
|
||||
alpha: 32
|
||||
target_modules: auto
|
||||
quantization: 4bit
|
||||
|
||||
output: ./output
|
||||
""",
|
||||
),
|
||||
"qwen3.6-35b-a3b-sft": RecipeMeta(
|
||||
model="Qwen/Qwen3.6-35B-A3B",
|
||||
task="sft",
|
||||
size="35B",
|
||||
tags=("qwen", "qwen3.6", "sft", "moe", "mixture-of-experts"),
|
||||
description="Qwen 3.6 35B-A3B MoE SFT (Apache-2.0, 3B active)",
|
||||
yaml_str="""\
|
||||
base: Qwen/Qwen3.6-35B-A3B
|
||||
task: sft
|
||||
|
||||
data:
|
||||
train: ./data/train.jsonl
|
||||
format: auto
|
||||
max_length: 4096
|
||||
|
||||
training:
|
||||
epochs: 3
|
||||
lr: 1e-4
|
||||
batch_size: auto
|
||||
gradient_accumulation_steps: 8
|
||||
lora:
|
||||
r: 16
|
||||
alpha: 32
|
||||
target_modules: auto
|
||||
quantization: 4bit
|
||||
moe_lora: true
|
||||
moe_aux_loss_coeff: 0.01
|
||||
|
||||
output: ./output
|
||||
""",
|
||||
),
|
||||
"deepseek-v4-flash-sft": RecipeMeta(
|
||||
model="deepseek-ai/DeepSeek-V4-Flash",
|
||||
task="sft",
|
||||
size="N/A",
|
||||
tags=("deepseek", "deepseek-v4", "sft", "moe", "mixture-of-experts"),
|
||||
description="DeepSeek V4 Flash MoE SFT (MIT, efficiency-tier)",
|
||||
yaml_str="""\
|
||||
base: deepseek-ai/DeepSeek-V4-Flash
|
||||
task: sft
|
||||
|
||||
data:
|
||||
train: ./data/train.jsonl
|
||||
format: auto
|
||||
max_length: 4096
|
||||
|
||||
training:
|
||||
epochs: 3
|
||||
lr: 1e-4
|
||||
batch_size: auto
|
||||
gradient_accumulation_steps: 8
|
||||
lora:
|
||||
r: 16
|
||||
alpha: 32
|
||||
target_modules: auto
|
||||
quantization: 4bit
|
||||
moe_lora: true
|
||||
moe_aux_loss_coeff: 0.01
|
||||
|
||||
output: ./output
|
||||
""",
|
||||
),
|
||||
"deepseek-v4-pro-sft": RecipeMeta(
|
||||
model="deepseek-ai/DeepSeek-V4-Pro",
|
||||
task="sft",
|
||||
size="N/A",
|
||||
tags=("deepseek", "deepseek-v4", "sft", "moe", "large", "multi-gpu"),
|
||||
description=(
|
||||
"DeepSeek V4 Pro flagship MoE SFT (MIT, 1.6T-class). "
|
||||
"Requires multi-node DeepSpeed."
|
||||
),
|
||||
yaml_str="""\
|
||||
base: deepseek-ai/DeepSeek-V4-Pro
|
||||
task: sft
|
||||
|
||||
data:
|
||||
train: ./data/train.jsonl
|
||||
format: auto
|
||||
max_length: 4096
|
||||
|
||||
training:
|
||||
epochs: 1
|
||||
lr: 5e-6
|
||||
batch_size: 1
|
||||
gradient_accumulation_steps: 32
|
||||
lora:
|
||||
r: 32
|
||||
alpha: 64
|
||||
target_modules: auto
|
||||
quantization: 4bit
|
||||
moe_lora: true
|
||||
moe_aux_loss_coeff: 0.01
|
||||
gradient_checkpointing: true
|
||||
|
||||
output: ./output
|
||||
""",
|
||||
),
|
||||
"glm-5.1-sft": RecipeMeta(
|
||||
model="zai-org/GLM-5.1",
|
||||
task="sft",
|
||||
size="754B",
|
||||
tags=("glm", "zai-org", "sft", "moe", "large", "multi-gpu"),
|
||||
description=(
|
||||
"GLM 5.1 MoE SFT (MIT, 754B). Multi-GPU / multi-node recommended."
|
||||
),
|
||||
yaml_str="""\
|
||||
base: zai-org/GLM-5.1
|
||||
task: sft
|
||||
|
||||
data:
|
||||
train: ./data/train.jsonl
|
||||
format: auto
|
||||
max_length: 8192
|
||||
|
||||
training:
|
||||
epochs: 1
|
||||
lr: 1e-5
|
||||
batch_size: 1
|
||||
gradient_accumulation_steps: 16
|
||||
lora:
|
||||
r: 32
|
||||
alpha: 64
|
||||
target_modules: auto
|
||||
quantization: 4bit
|
||||
moe_lora: true
|
||||
moe_aux_loss_coeff: 0.01
|
||||
gradient_checkpointing: true
|
||||
|
||||
output: ./output
|
||||
""",
|
||||
),
|
||||
"kimi-k2.5-sft": RecipeMeta(
|
||||
model="moonshotai/Kimi-K2.5",
|
||||
task="sft",
|
||||
size="1T",
|
||||
tags=("kimi", "moonshot", "sft", "moe", "large", "multi-gpu"),
|
||||
description=(
|
||||
"Kimi K2.5 MoE SFT (Modified MIT, ~1T / 32B active). "
|
||||
"Requires multi-node DeepSpeed."
|
||||
),
|
||||
yaml_str="""\
|
||||
base: moonshotai/Kimi-K2.5
|
||||
task: sft
|
||||
|
||||
data:
|
||||
train: ./data/train.jsonl
|
||||
format: auto
|
||||
max_length: 8192
|
||||
|
||||
training:
|
||||
epochs: 1
|
||||
lr: 1e-5
|
||||
batch_size: 1
|
||||
gradient_accumulation_steps: 16
|
||||
lora:
|
||||
r: 32
|
||||
alpha: 64
|
||||
target_modules: auto
|
||||
quantization: 4bit
|
||||
moe_lora: true
|
||||
moe_aux_loss_coeff: 0.01
|
||||
gradient_checkpointing: true
|
||||
|
||||
output: ./output
|
||||
""",
|
||||
),
|
||||
"kimi-k2.6-sft": RecipeMeta(
|
||||
model="moonshotai/Kimi-K2.6",
|
||||
task="sft",
|
||||
size="1T",
|
||||
tags=("kimi", "moonshot", "sft", "moe", "large", "multi-gpu"),
|
||||
description=(
|
||||
"Kimi K2.6 MoE SFT (Modified MIT, ~1T / 32B active). "
|
||||
"Requires multi-node DeepSpeed."
|
||||
),
|
||||
yaml_str="""\
|
||||
base: moonshotai/Kimi-K2.6
|
||||
task: sft
|
||||
|
||||
data:
|
||||
train: ./data/train.jsonl
|
||||
format: auto
|
||||
max_length: 8192
|
||||
|
||||
training:
|
||||
epochs: 1
|
||||
lr: 5e-6
|
||||
batch_size: 1
|
||||
gradient_accumulation_steps: 32
|
||||
lora:
|
||||
r: 32
|
||||
alpha: 64
|
||||
target_modules: auto
|
||||
quantization: 4bit
|
||||
moe_lora: true
|
||||
moe_aux_loss_coeff: 0.01
|
||||
gradient_checkpointing: true
|
||||
|
||||
output: ./output
|
||||
""",
|
||||
),
|
||||
"minimax-m3-sft": RecipeMeta(
|
||||
model="MiniMaxAI/MiniMax-M3",
|
||||
task="sft",
|
||||
size="428B",
|
||||
tags=("minimax", "sft", "moe", "large", "multi-gpu"),
|
||||
description=(
|
||||
"MiniMax M3 MoE SFT (428B / 23B active). MiniMax Community License "
|
||||
"- commercial use requires a separate agreement. Multi-GPU recommended."
|
||||
),
|
||||
yaml_str="""\
|
||||
base: MiniMaxAI/MiniMax-M3
|
||||
task: sft
|
||||
|
||||
data:
|
||||
train: ./data/train.jsonl
|
||||
format: auto
|
||||
max_length: 4096
|
||||
|
||||
training:
|
||||
epochs: 1
|
||||
lr: 1e-5
|
||||
batch_size: 1
|
||||
gradient_accumulation_steps: 16
|
||||
lora:
|
||||
r: 32
|
||||
alpha: 64
|
||||
target_modules: auto
|
||||
quantization: 4bit
|
||||
moe_lora: true
|
||||
moe_aux_loss_coeff: 0.01
|
||||
gradient_checkpointing: true
|
||||
|
||||
output: ./output
|
||||
""",
|
||||
),
|
||||
"mistral-large-3-sft": RecipeMeta(
|
||||
model="mistralai/Mistral-Large-3-675B-Instruct-2512",
|
||||
task="sft",
|
||||
size="675B",
|
||||
tags=("mistral", "mistral-large", "sft", "moe", "large", "multi-gpu"),
|
||||
description=(
|
||||
"Mistral Large 3 MoE SFT (Apache-2.0, 675B / 41B active, multimodal). "
|
||||
"Requires multi-node DeepSpeed."
|
||||
),
|
||||
yaml_str="""\
|
||||
base: mistralai/Mistral-Large-3-675B-Instruct-2512
|
||||
task: sft
|
||||
|
||||
data:
|
||||
train: ./data/train.jsonl
|
||||
format: auto
|
||||
max_length: 4096
|
||||
|
||||
training:
|
||||
epochs: 1
|
||||
lr: 5e-6
|
||||
batch_size: 1
|
||||
gradient_accumulation_steps: 32
|
||||
lora:
|
||||
r: 32
|
||||
alpha: 64
|
||||
target_modules: auto
|
||||
quantization: 4bit
|
||||
moe_lora: true
|
||||
moe_aux_loss_coeff: 0.01
|
||||
gradient_checkpointing: true
|
||||
|
||||
output: ./output
|
||||
""",
|
||||
),
|
||||
|
|
|
|||
|
|
@ -260,7 +260,7 @@ class TestV025NewRecipes:
|
|||
assert cfg.base == recipe.model
|
||||
assert cfg.task == recipe.task
|
||||
|
||||
def test_catalog_size_is_116(self):
|
||||
def test_catalog_size_is_133(self):
|
||||
"""Total catalog size — grew with each release.
|
||||
|
||||
v0.25.0 shipped 43 recipes (29 + 9 Part A + 2 Part B tools + 3 Part E MLX).
|
||||
|
|
@ -270,10 +270,11 @@ class TestV025NewRecipes:
|
|||
v0.52.0 added 6 (5 TTS + Falcon-E BitNet) -> 112.
|
||||
v0.53.5 added 1 (deepseek-v3-reasoning) -> 113.
|
||||
v0.62.0 added 3 (raft-llama3-8b, ra-dit-retriever, ra-dit-llama3-8b) -> 116.
|
||||
v0.71.24 added 17 (2026 model-family expansion) -> 133.
|
||||
"""
|
||||
from soup_cli.recipes.catalog import RECIPES
|
||||
|
||||
assert len(RECIPES) == 116
|
||||
assert len(RECIPES) == 133
|
||||
|
||||
def test_new_recipes_searchable(self):
|
||||
"""Search returns the new recipes via keyword/task filter."""
|
||||
|
|
|
|||
|
|
@ -0,0 +1,291 @@
|
|||
"""v0.71.24 — 2026 model-family recipe expansion (catalog 116 -> 133).
|
||||
|
||||
Pure config/data release: 17 new ready-made SFT recipes for the open-weight
|
||||
models released Feb-Jun 2026 (Qwen3.5 / Qwen3.6 / DeepSeek-V4 / GLM-5.1 /
|
||||
Kimi-K2.5/K2.6 / MiniMax-M3 / Mistral-Large-3), plus a stale-repo-ID fix for
|
||||
``glm-5-sft`` (``THUDM/glm-5`` -> ``zai-org/GLM-5``). CI validates each recipe by
|
||||
``load_config_from_string`` parse only (no network).
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import pytest
|
||||
import yaml
|
||||
|
||||
from soup_cli.config.loader import load_config_from_string
|
||||
from soup_cli.config.schema import SoupConfig
|
||||
from soup_cli.recipes.catalog import RECIPES, get_recipe, list_recipes, search_recipes
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Step A — 17 new SFT recipes (every base verified to resolve on Hugging Face)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
V07124_RECIPE_NAMES = [
|
||||
# Qwen3.5 family (Apache-2.0)
|
||||
"qwen3.5-0.8b-sft",
|
||||
"qwen3.5-2b-sft",
|
||||
"qwen3.5-4b-sft",
|
||||
"qwen3.5-9b-sft",
|
||||
"qwen3.5-27b-sft",
|
||||
"qwen3.5-35b-a3b-sft",
|
||||
"qwen3.5-122b-a10b-sft",
|
||||
"qwen3.5-397b-a17b-sft",
|
||||
# Qwen3.6 (Apache-2.0)
|
||||
"qwen3.6-27b-sft",
|
||||
"qwen3.6-35b-a3b-sft",
|
||||
# DeepSeek-V4 (MIT)
|
||||
"deepseek-v4-flash-sft",
|
||||
"deepseek-v4-pro-sft",
|
||||
# GLM (MIT, zai-org)
|
||||
"glm-5.1-sft",
|
||||
# Kimi (Modified MIT)
|
||||
"kimi-k2.5-sft",
|
||||
"kimi-k2.6-sft",
|
||||
# MiniMax (MiniMax Community License)
|
||||
"minimax-m3-sft",
|
||||
# Mistral Large 3 (Apache-2.0)
|
||||
"mistral-large-3-sft",
|
||||
]
|
||||
|
||||
# name -> expected base repo-ID (the verified Hugging Face repo)
|
||||
V07124_RECIPE_BASES = {
|
||||
"qwen3.5-0.8b-sft": "Qwen/Qwen3.5-0.8B",
|
||||
"qwen3.5-2b-sft": "Qwen/Qwen3.5-2B",
|
||||
"qwen3.5-4b-sft": "Qwen/Qwen3.5-4B",
|
||||
"qwen3.5-9b-sft": "Qwen/Qwen3.5-9B",
|
||||
"qwen3.5-27b-sft": "Qwen/Qwen3.5-27B",
|
||||
"qwen3.5-35b-a3b-sft": "Qwen/Qwen3.5-35B-A3B",
|
||||
"qwen3.5-122b-a10b-sft": "Qwen/Qwen3.5-122B-A10B",
|
||||
"qwen3.5-397b-a17b-sft": "Qwen/Qwen3.5-397B-A17B",
|
||||
"qwen3.6-27b-sft": "Qwen/Qwen3.6-27B",
|
||||
"qwen3.6-35b-a3b-sft": "Qwen/Qwen3.6-35B-A3B",
|
||||
"deepseek-v4-flash-sft": "deepseek-ai/DeepSeek-V4-Flash",
|
||||
"deepseek-v4-pro-sft": "deepseek-ai/DeepSeek-V4-Pro",
|
||||
"glm-5.1-sft": "zai-org/GLM-5.1",
|
||||
"kimi-k2.5-sft": "moonshotai/Kimi-K2.5",
|
||||
"kimi-k2.6-sft": "moonshotai/Kimi-K2.6",
|
||||
"minimax-m3-sft": "MiniMaxAI/MiniMax-M3",
|
||||
"mistral-large-3-sft": "mistralai/Mistral-Large-3-675B-Instruct-2512",
|
||||
}
|
||||
|
||||
# The MoE bases added in v0.71.24 — each recipe must enable moe_lora + aux loss.
|
||||
# (Scope is intentionally limited to recipes added in THIS release, not every MoE
|
||||
# recipe in the catalog.)
|
||||
V07124_MOE_RECIPES = [
|
||||
"qwen3.5-35b-a3b-sft",
|
||||
"qwen3.5-122b-a10b-sft",
|
||||
"qwen3.5-397b-a17b-sft",
|
||||
"qwen3.6-35b-a3b-sft",
|
||||
"deepseek-v4-flash-sft",
|
||||
"deepseek-v4-pro-sft",
|
||||
"glm-5.1-sft",
|
||||
"kimi-k2.5-sft",
|
||||
"kimi-k2.6-sft",
|
||||
"minimax-m3-sft",
|
||||
"mistral-large-3-sft",
|
||||
]
|
||||
|
||||
|
||||
class TestV07124Recipes:
|
||||
def test_recipe_count_target(self) -> None:
|
||||
assert len(V07124_RECIPE_NAMES) == 17
|
||||
|
||||
@pytest.mark.parametrize("name", V07124_RECIPE_NAMES)
|
||||
def test_recipe_registered(self, name: str) -> None:
|
||||
assert name in RECIPES, f"Recipe {name!r} missing from catalog"
|
||||
assert get_recipe(name) is not None
|
||||
|
||||
@pytest.mark.parametrize("name", V07124_RECIPE_NAMES)
|
||||
def test_recipe_base_matches_verified_repo(self, name: str) -> None:
|
||||
assert RECIPES[name].model == V07124_RECIPE_BASES[name]
|
||||
|
||||
@pytest.mark.parametrize("name", V07124_RECIPE_NAMES)
|
||||
def test_recipe_is_sft(self, name: str) -> None:
|
||||
assert RECIPES[name].task == "sft"
|
||||
|
||||
@pytest.mark.parametrize("name", V07124_RECIPE_NAMES)
|
||||
def test_recipe_metadata_well_formed(self, name: str) -> None:
|
||||
meta = RECIPES[name]
|
||||
assert meta.model, f"{name}: model is empty"
|
||||
assert meta.size, f"{name}: size is empty"
|
||||
assert meta.tags, f"{name}: tags is empty"
|
||||
assert meta.description, f"{name}: description is empty"
|
||||
assert meta.yaml_str, f"{name}: yaml_str is empty"
|
||||
|
||||
@pytest.mark.parametrize("name", V07124_RECIPE_NAMES)
|
||||
def test_recipe_yaml_parses_as_soup_config(self, name: str) -> None:
|
||||
cfg = load_config_from_string(RECIPES[name].yaml_str)
|
||||
assert isinstance(cfg, SoupConfig)
|
||||
assert cfg.base == RECIPES[name].model
|
||||
assert cfg.task == "sft"
|
||||
|
||||
@pytest.mark.parametrize("name", V07124_RECIPE_NAMES)
|
||||
def test_recipe_yaml_is_safe_loadable(self, name: str) -> None:
|
||||
parsed = yaml.safe_load(RECIPES[name].yaml_str)
|
||||
assert isinstance(parsed, dict)
|
||||
assert "base" in parsed and "task" in parsed
|
||||
# The inline ``base:`` / ``task:`` in the YAML must match the RecipeMeta.
|
||||
assert parsed["base"] == RECIPES[name].model
|
||||
assert parsed["task"] == RECIPES[name].task
|
||||
|
||||
@pytest.mark.parametrize("name", V07124_RECIPE_NAMES)
|
||||
def test_recipe_model_id_no_null_or_whitespace(self, name: str) -> None:
|
||||
meta = RECIPES[name]
|
||||
assert "\x00" not in meta.model
|
||||
assert " " not in meta.model
|
||||
# No path-traversal segments or shell metacharacters in a repo id.
|
||||
assert ".." not in meta.model, f"path-traversal segment in {meta.model!r}"
|
||||
assert not any(c in meta.model for c in ';|&$`<>(){}*?!\\\n\t'), (
|
||||
f"shell metacharacter in {meta.model!r}"
|
||||
)
|
||||
parts = meta.model.split("/")
|
||||
assert len(parts) == 2
|
||||
assert all(p for p in parts), f"empty component in {meta.model!r}"
|
||||
|
||||
@pytest.mark.parametrize("name", V07124_RECIPE_NAMES)
|
||||
def test_recipe_max_length_within_bounds(self, name: str) -> None:
|
||||
cfg = load_config_from_string(RECIPES[name].yaml_str)
|
||||
assert 64 <= cfg.data.max_length <= 1_048_576
|
||||
|
||||
@pytest.mark.parametrize("name", V07124_MOE_RECIPES)
|
||||
def test_moe_recipes_enable_moe_lora(self, name: str) -> None:
|
||||
cfg = load_config_from_string(RECIPES[name].yaml_str)
|
||||
assert cfg.training.moe_lora is True, f"{name} should set moe_lora: true"
|
||||
|
||||
@pytest.mark.parametrize("name", V07124_MOE_RECIPES)
|
||||
def test_moe_recipes_set_aux_loss_coeff(self, name: str) -> None:
|
||||
# Every new MoE recipe sets the aux-loss coefficient explicitly (0.01) so
|
||||
# the whole batch is uniformly self-documenting (review HIGH-1).
|
||||
cfg = load_config_from_string(RECIPES[name].yaml_str)
|
||||
assert abs(cfg.training.moe_aux_loss_coeff - 0.01) < 1e-9, (
|
||||
f"{name} should set moe_aux_loss_coeff: 0.01, got "
|
||||
f"{cfg.training.moe_aux_loss_coeff}"
|
||||
)
|
||||
|
||||
@pytest.mark.parametrize("name", sorted(set(V07124_RECIPE_NAMES) - set(V07124_MOE_RECIPES)))
|
||||
def test_dense_recipes_do_not_set_moe_lora(self, name: str) -> None:
|
||||
# The complement of the MoE list: a dense recipe must NOT enable moe_lora
|
||||
# (guards against pasting a MoE block into a dense recipe).
|
||||
cfg = load_config_from_string(RECIPES[name].yaml_str)
|
||||
assert cfg.training.moe_lora is not True, (
|
||||
f"{name} is a dense recipe but has moe_lora: true"
|
||||
)
|
||||
|
||||
@pytest.mark.parametrize("name", V07124_MOE_RECIPES)
|
||||
def test_moe_recipes_tagged_moe(self, name: str) -> None:
|
||||
assert "moe" in RECIPES[name].tags, (
|
||||
f"{name} is a MoE recipe but lacks the 'moe' tag"
|
||||
)
|
||||
|
||||
@pytest.mark.parametrize("name", [n for n in V07124_RECIPE_NAMES if n.startswith("qwen")])
|
||||
def test_qwen_recipes_tagged_qwen(self, name: str) -> None:
|
||||
assert "qwen" in RECIPES[name].tags, f"{name} lacks the 'qwen' tag"
|
||||
|
||||
def test_search_by_task_sft_includes_all_new_recipes(self) -> None:
|
||||
sft_models = {r.model for r in search_recipes(task="sft")}
|
||||
for name in V07124_RECIPE_NAMES:
|
||||
assert RECIPES[name].model in sft_models, (
|
||||
f"{name} not returned by search_recipes(task='sft')"
|
||||
)
|
||||
|
||||
def test_kimi_k2_5_size_known(self) -> None:
|
||||
# K2.5 is a ~1T/32B MoE (verified on the model card) — not "N/A".
|
||||
assert RECIPES["kimi-k2.5-sft"].size == "1T"
|
||||
|
||||
def test_kimi_k2_6_size_known(self) -> None:
|
||||
# K2.6 is a ~1T/32B MoE (verified on the model card).
|
||||
assert RECIPES["kimi-k2.6-sft"].size == "1T"
|
||||
|
||||
def test_minimax_recipe_notes_community_license(self) -> None:
|
||||
desc = RECIPES["minimax-m3-sft"].description.lower()
|
||||
assert "minimax" in desc
|
||||
assert "license" in desc
|
||||
# commercial-use caveat must be surfaced
|
||||
assert "commercial" in desc
|
||||
|
||||
def test_mistral_large_3_notes_apache_license(self) -> None:
|
||||
# Plan guessed MRL; the published card is Apache-2.0 — describe accurately.
|
||||
desc = RECIPES["mistral-large-3-sft"].description.lower()
|
||||
assert "apache" in desc
|
||||
|
||||
@pytest.mark.parametrize("name", ["kimi-k2.5-sft", "kimi-k2.6-sft"])
|
||||
def test_kimi_recipes_note_modified_mit(self, name: str) -> None:
|
||||
assert "mit" in RECIPES[name].description.lower()
|
||||
|
||||
def test_deepseek_v4_sizes_intentionally_na(self) -> None:
|
||||
# DeepSeek has not publicly confirmed the V4 parameter counts; "N/A" is the
|
||||
# honest value. This documents the deliberate choice (vs Kimi K2.5/K2.6,
|
||||
# whose ~1T sizes ARE on the card).
|
||||
assert RECIPES["deepseek-v4-flash-sft"].size == "N/A"
|
||||
assert RECIPES["deepseek-v4-pro-sft"].size == "N/A"
|
||||
|
||||
@pytest.mark.parametrize("name", V07124_RECIPE_NAMES)
|
||||
def test_recipe_searchable(self, name: str) -> None:
|
||||
# The recipe key is part of the searchable text, so a query for the recipe's
|
||||
# own name must return that exact recipe (deterministic — no token heuristic).
|
||||
results = search_recipes(query=name)
|
||||
assert any(r.model == RECIPES[name].model for r in results), (
|
||||
f"{name} not returned by search_recipes(query={name!r})"
|
||||
)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Step B — stale repo-ID fix: glm-5-sft (THUDM/glm-5 -> zai-org/GLM-5)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestGlm5RepoIdFix:
|
||||
def test_glm5_uses_zai_org(self) -> None:
|
||||
meta = RECIPES["glm-5-sft"]
|
||||
assert meta.model == "zai-org/GLM-5"
|
||||
assert "THUDM" not in meta.model
|
||||
|
||||
def test_glm5_yaml_base_updated(self) -> None:
|
||||
meta = RECIPES["glm-5-sft"]
|
||||
parsed = yaml.safe_load(meta.yaml_str)
|
||||
assert parsed["base"] == "zai-org/GLM-5"
|
||||
assert "THUDM/glm-5" not in meta.yaml_str
|
||||
|
||||
def test_glm5_old_key_still_exists(self) -> None:
|
||||
# The fix RENAMES the base field; it must NOT delete the old recipe key.
|
||||
assert "glm-5-sft" in RECIPES, "glm-5-sft was accidentally removed"
|
||||
assert "glm-5.1-sft" in RECIPES, "glm-5.1-sft is the new v0.71.24 recipe"
|
||||
|
||||
def test_glm5_and_glm51_are_distinct_entries(self) -> None:
|
||||
assert RECIPES["glm-5-sft"].model != RECIPES["glm-5.1-sft"].model
|
||||
|
||||
def test_no_recipe_references_thudm_glm5(self) -> None:
|
||||
for name, meta in RECIPES.items():
|
||||
assert "THUDM/glm-5" not in meta.model, (
|
||||
f"{name}: model still references THUDM/glm-5"
|
||||
)
|
||||
assert "THUDM/glm-5" not in meta.yaml_str, (
|
||||
f"{name}: yaml_str still references THUDM/glm-5"
|
||||
)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Catalog count — 116 -> 133
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestCatalogCount:
|
||||
def test_total_recipe_count_is_133(self) -> None:
|
||||
assert len(RECIPES) == 133
|
||||
|
||||
def test_list_recipes_matches_dict(self) -> None:
|
||||
assert len(list_recipes()) == len(RECIPES)
|
||||
|
||||
def test_all_recipes_parse(self) -> None:
|
||||
# Defence-in-depth — the whole catalog must still parse after the additions.
|
||||
for name, meta in RECIPES.items():
|
||||
cfg = load_config_from_string(meta.yaml_str)
|
||||
assert cfg.base, name
|
||||
|
||||
def test_all_recipes_yaml_base_matches_model(self) -> None:
|
||||
# Catalog-wide integrity invariant — the inline ``base:`` must equal
|
||||
# RecipeMeta.model for EVERY recipe (catches a half-applied repo-ID fix).
|
||||
for name, meta in RECIPES.items():
|
||||
parsed = yaml.safe_load(meta.yaml_str)
|
||||
assert parsed.get("base") == meta.model, (
|
||||
f"{name}: yaml base {parsed.get('base')!r} != model {meta.model!r}"
|
||||
)
|
||||
Loading…
Reference in New Issue