mirror of https://github.com/razor-ai/soup.git
Release v0.10.0: Phase 6.1 - Community (CONTRIBUTING, CODE_OF_CONDUCT, SECURITY, examples, FUNDING)
This commit is contained in:
Alpamys
parent
cc030df391
commit
e0f8e921bd
|
|
@ -0,0 +1,2 @@
|
|||
github: MakazhanAlpamys
|
||||
buy_me_a_coffee: makazhanalpamys
|
||||
|
|
@ -89,6 +89,8 @@ soup train --config soup.yaml
|
|||
|
||||
**Web UI:** `commands/ui.py` launches a local web interface via `soup ui`. `ui/app.py` creates a FastAPI app with REST API endpoints for experiment management (`/api/runs`, `/api/runs/{id}/metrics`), config validation (`/api/config/validate`), training control (`/api/train/start`, `/api/train/status`, `/api/train/stop`), data inspection (`/api/data/inspect`), system info (`/api/system`), and templates (`/api/templates`). `ui/static/` contains a self-contained SPA (HTML/CSS/JS) with four pages: Dashboard (experiments list, loss charts via Chart.js), New Training (config editor with templates), Data Explorer (browse datasets), and Model Chat (chat with a `soup serve` instance). Config validation uses `config/loader.py`'s `load_config_from_string()`. Requires `pip install 'soup-cli[ui]'`. Auto-opens browser on launch (disable with `--no-browser`).
|
||||
|
||||
**Community & Examples:** `CONTRIBUTING.md` provides full contributor guide (fork, test, lint, code style, PR process). `CODE_OF_CONDUCT.md` uses Contributor Covenant v2.1. `SECURITY.md` documents responsive disclosure policy (48h response, severity levels). `.github/FUNDING.yml` adds GitHub Sponsors + Buy Me a Coffee buttons. `examples/` folder includes 7 real YAML configs (SFT, DPO, GRPO, Vision, RLHF 3-stage), 3 sample JSONL datasets, and comprehensive README with quick-start examples.
|
||||
|
||||
**Confirmation prompts:** `commands/train.py` and `commands/sweep.py` ask for confirmation before starting. Skip with `--yes` / `-y`.
|
||||
|
||||
**Version:** `cli.py` `version()` command supports `--full` flag that shows version, Python version, GPU backend, and installed optional extras in one line.
|
||||
|
|
|
|||
|
|
@ -0,0 +1,77 @@
|
|||
# Contributor Covenant Code of Conduct
|
||||
|
||||
## Our Pledge
|
||||
|
||||
We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation.
|
||||
|
||||
We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community.
|
||||
|
||||
## Our Standards
|
||||
|
||||
Examples of behavior that contributes to a positive environment for our community include:
|
||||
|
||||
- Demonstrating empathy and kindness toward other people
|
||||
- Being respectful of differing opinions, viewpoints, and experiences
|
||||
- Giving and gracefully accepting constructive feedback
|
||||
- Accepting responsibility and apologizing to those affected by our mistakes, and learning from the experience
|
||||
- Focusing on what is best not just for us as individuals, but for the overall community
|
||||
|
||||
Examples of unacceptable behavior include:
|
||||
|
||||
- The use of sexualized language or imagery, and sexual attention or advances of any kind
|
||||
- Trolling, insulting or derogatory comments, and personal or political attacks
|
||||
- Public or private harassment
|
||||
- Publishing others' private information, such as a physical or email address, without their explicit permission
|
||||
- Other conduct which could reasonably be considered inappropriate in a professional setting
|
||||
|
||||
## Enforcement Responsibilities
|
||||
|
||||
Community leaders are responsible for clarifying and enforcing our standards of acceptable behavior and will take appropriate and fair corrective action in response to any behavior that they deem inappropriate, threatening, offensive, or harmful.
|
||||
|
||||
Community leaders have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, and will communicate reasons for moderation decisions when appropriate.
|
||||
|
||||
## Scope
|
||||
|
||||
This Code of Conduct applies within all community spaces, and also applies when an individual is officially representing the community in public spaces. Examples of representing our community include using an official e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event.
|
||||
|
||||
## Enforcement
|
||||
|
||||
Instances of abusive, harassing, or otherwise unacceptable behavior may be reported to the community leaders responsible for enforcement at **conduct@soup-cli.dev**. All complaints will be reviewed and investigated promptly and fairly.
|
||||
|
||||
All community leaders are obligated to respect the privacy and security of the reporter of any incident.
|
||||
|
||||
## Enforcement Guidelines
|
||||
|
||||
Community leaders will follow these Community Impact Guidelines in determining the consequences for any action they deem in violation of this Code of Conduct:
|
||||
|
||||
### 1. Correction
|
||||
|
||||
**Community Impact**: Use of inappropriate language or other behavior deemed unprofessional or unwelcome in the community.
|
||||
|
||||
**Consequence**: A private, written warning from community leadership, providing clarity around the nature of the violation and an explanation of why the behavior was inappropriate. A public apology may be requested.
|
||||
|
||||
### 2. Warning
|
||||
|
||||
**Community Impact**: A violation through a single incident or series of actions.
|
||||
|
||||
**Consequence**: A warning with consequences for continued behavior. No interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, for a specified period of time. This includes avoiding interactions in community spaces as well as external channels like social media. Violating these terms may lead to a temporary or permanent ban.
|
||||
|
||||
### 3. Temporary Ban
|
||||
|
||||
**Community Impact**: A serious violation of community standards, including sustained inappropriate behavior.
|
||||
|
||||
**Consequence**: A temporary ban from any sort of interaction or public communication with the community for a specified period of time. No public or private interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, is allowed during this period. Violating these terms may lead to a permanent ban.
|
||||
|
||||
### 4. Permanent Ban
|
||||
|
||||
**Community Impact**: Demonstrating a pattern of violation of community standards, including sustained inappropriate behavior, harassment of an individual, or aggression toward or disparagement of classes of individuals.
|
||||
|
||||
**Consequence**: A permanent ban from any sort of public interaction within the community.
|
||||
|
||||
## Attribution
|
||||
|
||||
This Code of Conduct is adapted from the [Contributor Covenant](https://www.contributor-covenant.org), version 2.1, available at https://www.contributor-covenant.org/version/2_1/code_of_conduct.html.
|
||||
|
||||
Community Impact Guidelines were inspired by [Mozilla's code of conduct enforcement ladder](https://github.com/mozilla/diversity).
|
||||
|
||||
For answers to common questions about this code of conduct, see the FAQ at https://www.contributor-covenant.org/faq. Translations are available at https://www.contributor-covenant.org/translations.
|
||||
|
|
@ -0,0 +1,319 @@
|
|||
# Contributing to Soup
|
||||
|
||||
Thank you for your interest in contributing to Soup! We welcome bug reports, feature requests, and pull requests from the community.
|
||||
|
||||
## Getting Started
|
||||
|
||||
### 1. Fork & Clone
|
||||
|
||||
```bash
|
||||
git clone https://github.com/YOUR-USERNAME/Soup.git
|
||||
cd Soup
|
||||
```
|
||||
|
||||
### 2. Set Up Development Environment
|
||||
|
||||
Install the project in editable mode with dev dependencies:
|
||||
|
||||
```bash
|
||||
pip install -e ".[dev]"
|
||||
```
|
||||
|
||||
This installs:
|
||||
- `pytest` for testing
|
||||
- `ruff` for linting
|
||||
- `pytest-cov` for coverage
|
||||
- `httpx` for HTTP testing
|
||||
|
||||
### 3. Verify Setup
|
||||
|
||||
Run the test suite to confirm everything works:
|
||||
|
||||
```bash
|
||||
pytest tests/ -v --tb=short
|
||||
```
|
||||
|
||||
Run the linter:
|
||||
|
||||
```bash
|
||||
ruff check soup_cli/ tests/
|
||||
```
|
||||
|
||||
## Code Style
|
||||
|
||||
We use **ruff** for all code style and linting. Before committing, run:
|
||||
|
||||
```bash
|
||||
# Check for issues
|
||||
ruff check soup_cli/ tests/
|
||||
|
||||
# Auto-fix issues
|
||||
ruff check --fix soup_cli/ tests/
|
||||
```
|
||||
|
||||
### Style Guidelines
|
||||
|
||||
- **Line length:** 100 characters (enforced by ruff)
|
||||
- **Imports:** Sorted and organized (ruff I rule)
|
||||
- **Naming:** No single-letter variable names (ruff E741) — use `entry`, `part`, `length` instead of `l`, `p`, etc.
|
||||
- **Lazy imports:** Heavy dependencies (torch, transformers, peft, trl, etc.) should be imported inside functions, not at module level, to keep the CLI responsive
|
||||
- **Config validation:** Always use Pydantic v2 with `BaseModel` and `Field`
|
||||
- **Output:** Use `rich.console.Console` for all output — never bare `print()`
|
||||
- **Type hints:** Always include type hints for function parameters and return values
|
||||
|
||||
Example:
|
||||
|
||||
```python
|
||||
# ❌ WRONG
|
||||
from torch import cuda
|
||||
import transformers
|
||||
|
||||
def train():
|
||||
print("Starting training")
|
||||
model = transformers.AutoModel.from_pretrained("llama-7b")
|
||||
|
||||
# ✅ CORRECT
|
||||
def train():
|
||||
from torch import cuda
|
||||
import transformers
|
||||
|
||||
console = Console()
|
||||
console.print("Starting training")
|
||||
model = transformers.AutoModel.from_pretrained("llama-7b")
|
||||
```
|
||||
|
||||
## Project Structure
|
||||
|
||||
Key directories:
|
||||
|
||||
```
|
||||
soup_cli/
|
||||
cli.py - Main entry point, command routing
|
||||
commands/ - Command implementations (train, chat, eval, etc.)
|
||||
config/ - Config schema (schema.py) and loader (loader.py)
|
||||
data/ - Data loading and format conversion
|
||||
trainer/ - Training wrappers (SFT, DPO, GRPO, PPO, reward_model)
|
||||
monitoring/ - Callbacks and live dashboard
|
||||
experiment/ - SQLite experiment tracking
|
||||
utils/ - GPU detection, batch size estimation, error handling
|
||||
ui/ - Web UI (FastAPI + HTML/JS)
|
||||
|
||||
templates/ - YAML config templates (chat, code, medical, vision, rlhf, reasoning)
|
||||
tests/ - Test suite (40+ files, 600+ tests)
|
||||
examples/ - Real-world config examples and datasets
|
||||
```
|
||||
|
||||
## Running Tests
|
||||
|
||||
### All Tests
|
||||
|
||||
```bash
|
||||
pytest tests/ -v --tb=short
|
||||
```
|
||||
|
||||
### Single Test File
|
||||
|
||||
```bash
|
||||
pytest tests/test_config.py -v
|
||||
```
|
||||
|
||||
### Single Test
|
||||
|
||||
```bash
|
||||
pytest tests/test_data.py::test_detect_alpaca_format -v
|
||||
```
|
||||
|
||||
### With Coverage
|
||||
|
||||
```bash
|
||||
pytest tests/ --cov=soup_cli --cov-report=html
|
||||
```
|
||||
|
||||
### Test Categories
|
||||
|
||||
- `test_config.py` — Config loading and validation
|
||||
- `test_data.py` — Data format detection and conversion
|
||||
- `test_trainer_*.py` — Individual trainer tests
|
||||
- `test_smoke_train.py` — Full pipeline tests (GPU required)
|
||||
- `test_cli.py` — Command-line interface tests
|
||||
- `test_errors.py` — Error message handling
|
||||
- `test_*_command.py` — Specific command tests (chat, push, eval, etc.)
|
||||
|
||||
## Making Changes
|
||||
|
||||
### 1. Create a Branch
|
||||
|
||||
```bash
|
||||
git checkout -b feature/your-feature-name
|
||||
# or
|
||||
git checkout -b fix/your-bug-fix
|
||||
```
|
||||
|
||||
### 2. Make Your Changes
|
||||
|
||||
- Write code following the style guidelines above
|
||||
- Add tests for new functionality
|
||||
- Update docstrings and comments
|
||||
- Keep commits focused and logical
|
||||
|
||||
### 3. Run Tests & Lint
|
||||
|
||||
Before pushing, ensure everything passes:
|
||||
|
||||
```bash
|
||||
# Lint first
|
||||
ruff check --fix soup_cli/ tests/
|
||||
|
||||
# Then run tests
|
||||
pytest tests/ -v --tb=short
|
||||
```
|
||||
|
||||
If you've added new test files, increase the test count in `plan.md`.
|
||||
|
||||
### 4. Commit
|
||||
|
||||
Write clear, descriptive commit messages:
|
||||
|
||||
```bash
|
||||
git add .
|
||||
git commit -m "Add feature: descriptive message"
|
||||
```
|
||||
|
||||
### 5. Push & Open a PR
|
||||
|
||||
```bash
|
||||
git push origin feature/your-feature-name
|
||||
```
|
||||
|
||||
Then open a pull request on GitHub with:
|
||||
- Clear title describing the change
|
||||
- Description of what and why
|
||||
- Reference any related issues (e.g., "Closes #123")
|
||||
- Test results
|
||||
|
||||
## Submitting a Pull Request
|
||||
|
||||
### PR Template
|
||||
|
||||
Please use the following structure:
|
||||
|
||||
```markdown
|
||||
## What's this PR about?
|
||||
|
||||
Brief description of the change.
|
||||
|
||||
## Type of Change
|
||||
|
||||
- [ ] Bug fix
|
||||
- [ ] New feature
|
||||
- [ ] Documentation
|
||||
- [ ] Performance improvement
|
||||
|
||||
## Testing
|
||||
|
||||
Describe how you tested this (e.g., `pytest tests/test_X.py -v`).
|
||||
|
||||
## Checklist
|
||||
|
||||
- [ ] Linting passes: `ruff check soup_cli/ tests/`
|
||||
- [ ] Tests pass: `pytest tests/ -v`
|
||||
- [ ] New tests added for new functionality
|
||||
- [ ] Docstrings and comments added
|
||||
- [ ] No breaking changes (or documented)
|
||||
|
||||
## Related Issues
|
||||
|
||||
Closes #123 (if applicable)
|
||||
```
|
||||
|
||||
## Architecture & Design Decisions
|
||||
|
||||
### Lazy Imports for Speed
|
||||
|
||||
Heavy ML imports (torch, transformers, trl) are imported inside command handlers so the CLI stays fast. Users can run `soup version` or `soup --help` instantly without waiting for PyTorch to load.
|
||||
|
||||
### Pydantic for Config Validation
|
||||
|
||||
All YAML configs are validated using Pydantic v2 models. These models are the single source of truth for valid fields and defaults. See `config/schema.py`.
|
||||
|
||||
### Trainers as Wrappers
|
||||
|
||||
`trainer/sft.py`, `trainer/dpo.py`, `trainer/grpo.py`, `trainer/ppo.py` wrap HuggingFace TRL trainers with:
|
||||
- Auto quantization (BitsAndBytes, torchao QAT)
|
||||
- Auto LoRA setup (PEFT)
|
||||
- Auto batch size estimation
|
||||
- Progress bar integration
|
||||
|
||||
### Experiment Tracking is SQLite
|
||||
|
||||
No external dependencies required. All runs, metrics, and eval results go to `~/.soup/experiments.db`.
|
||||
|
||||
### Data Format Normalization
|
||||
|
||||
Multiple formats (Alpaca, ShareGPT, ChatML, LLaVA, ShareGPT4V) are normalized to a unified `{"messages": [...]}` structure in `data/formats.py`.
|
||||
|
||||
## Adding a New Feature
|
||||
|
||||
### 1. New Training Task Type
|
||||
|
||||
If adding a new training algorithm (e.g., DPO, GRPO):
|
||||
|
||||
1. Create `trainer/your_trainer.py` with a class inheriting from `BaseTrainer`
|
||||
2. Add Pydantic config class to `config/schema.py`
|
||||
3. Add template to `templates/your.yaml` and `config/schema.py`
|
||||
4. Update `commands/train.py` to route to your trainer
|
||||
5. Add 30+ tests in `tests/test_your_trainer.py`
|
||||
6. Update `CLAUDE.md` and `README.md`
|
||||
|
||||
### 2. New Data Format
|
||||
|
||||
1. Add detection and conversion logic to `data/formats.py`
|
||||
2. Add tests in `tests/test_formats.py`
|
||||
3. Update `data/loader.py` if needed
|
||||
4. Document in `CLAUDE.md`
|
||||
|
||||
### 3. New Command
|
||||
|
||||
1. Create `commands/your_command.py` with a handler function
|
||||
2. Register in `soup_cli/cli.py` with `@app.command()`
|
||||
3. Add tests in `tests/test_your_command.py`
|
||||
4. Update help text and README
|
||||
|
||||
## CI/CD
|
||||
|
||||
GitHub Actions runs on every push:
|
||||
- **ruff** linting (must pass)
|
||||
- **pytest** on Python 3.9, 3.11, 3.12 (must pass)
|
||||
|
||||
See `.github/workflows/ci.yml`.
|
||||
|
||||
## Releases
|
||||
|
||||
The project follows semantic versioning: `MAJOR.MINOR.PATCH`
|
||||
|
||||
### Version Bump Process
|
||||
|
||||
1. Update version in `pyproject.toml` and `soup_cli/__init__.py`
|
||||
2. Run full test suite and linting
|
||||
3. Update `CLAUDE.md`, `README.md`, `plan.md`
|
||||
4. Commit with message: `Release v0.X.0`
|
||||
5. Tag: `git tag v0.X.0 && git push --tags`
|
||||
6. GitHub Actions auto-publishes to PyPI
|
||||
|
||||
See `CLAUDE.md` for the complete release checklist.
|
||||
|
||||
## Community
|
||||
|
||||
- **Issues:** Report bugs and request features on [GitHub Issues](https://github.com/MakazhanAlpamys/Soup/issues)
|
||||
- **Discussions:** Ask questions on [GitHub Discussions](https://github.com/MakazhanAlpamys/Soup/discussions)
|
||||
- **Code of Conduct:** Please read [CODE_OF_CONDUCT.md](CODE_OF_CONDUCT.md)
|
||||
- **Security:** Report security issues to [SECURITY.md](SECURITY.md)
|
||||
|
||||
## Questions?
|
||||
|
||||
- Check the [README](README.md) for quick start and features
|
||||
- Check [CLAUDE.md](CLAUDE.md) for architecture details
|
||||
- Open a GitHub Discussion for questions
|
||||
- Join the community on Reddit ([r/LocalLLaMA](https://www.reddit.com/r/LocalLLaMA/))
|
||||
|
||||
Thank you for contributing! 🍲
|
||||
|
|
@ -0,0 +1,150 @@
|
|||
# Security Policy
|
||||
|
||||
## Supported Versions
|
||||
|
||||
We provide security updates for the following versions:
|
||||
|
||||
- **Latest minor version:** Active support (e.g., v0.9.x)
|
||||
- **Previous minor versions:** Bug-fix support only
|
||||
- **Versions older than 3 minor versions:** No support
|
||||
|
||||
Example:
|
||||
- v0.9.0-0.9.x → Full support (latest)
|
||||
- v0.8.0-0.8.x → Bug-fix support only
|
||||
- v0.7.x and below → No support
|
||||
|
||||
## Reporting a Vulnerability
|
||||
|
||||
**Do not** open a public issue or pull request for security vulnerabilities.
|
||||
|
||||
Instead, email your findings to **security@soup-cli.dev** with:
|
||||
|
||||
1. **Description**: A clear explanation of the vulnerability
|
||||
2. **Steps to Reproduce**: How to trigger or demonstrate the issue
|
||||
3. **Affected Versions**: Which Soup versions are impacted
|
||||
4. **Suggested Fix** (optional): Any proposed solutions
|
||||
5. **Contact Info**: Your email for follow-up (optional)
|
||||
|
||||
### What to Include
|
||||
|
||||
```
|
||||
To: security@soup-cli.dev
|
||||
Subject: Security Vulnerability Report: [Brief Title]
|
||||
|
||||
Description:
|
||||
[Explain the vulnerability in detail]
|
||||
|
||||
Affected Component:
|
||||
[e.g., data/loader.py, trainer/sft.py, etc.]
|
||||
|
||||
Steps to Reproduce:
|
||||
1. [Step 1]
|
||||
2. [Step 2]
|
||||
3. ...
|
||||
|
||||
Impact:
|
||||
[What could go wrong? Data exposure? RCE? DoS?]
|
||||
|
||||
Suggested Fix (optional):
|
||||
[Your proposed solution, if any]
|
||||
```
|
||||
|
||||
## Response Timeline
|
||||
|
||||
- **Initial Response**: Within 48 hours
|
||||
- **Assessment**: 1-3 business days
|
||||
- **Fix Development**: Varies by severity
|
||||
- **Patch Release**: As soon as possible after fix verification
|
||||
- **Public Disclosure**: Coordinated with reporter (typically 90 days after patch release)
|
||||
|
||||
## Severity Levels
|
||||
|
||||
- **Critical**: Remote code execution, data exposure, complete compromise (patch within 24-48 hours)
|
||||
- **High**: Authentication bypass, privilege escalation, denial of service (patch within 1 week)
|
||||
- **Medium**: Information disclosure, partial compromise (patch within 2 weeks)
|
||||
- **Low**: Minor issues with limited impact (patch in next regular release)
|
||||
|
||||
## Security Best Practices
|
||||
|
||||
When using Soup, follow these practices to stay secure:
|
||||
|
||||
### 1. Keep Soup Updated
|
||||
|
||||
```bash
|
||||
pip install --upgrade soup-cli
|
||||
```
|
||||
|
||||
### 2. Protect API Keys
|
||||
|
||||
Never commit API keys or secrets to version control. Use environment variables:
|
||||
|
||||
```bash
|
||||
export HUGGINGFACE_TOKEN=your_token_here
|
||||
export WANDB_API_KEY=your_key_here
|
||||
soup train
|
||||
```
|
||||
|
||||
### 3. Validate Data
|
||||
|
||||
- Only use trusted datasets
|
||||
- Verify checksums for large datasets
|
||||
- Inspect data for malicious content before training
|
||||
|
||||
### 4. Model Permissions
|
||||
|
||||
- Be cautious when downloading models from untrusted sources
|
||||
- Use model hub providers with verified publishers (HuggingFace, Meta, etc.)
|
||||
- Keep track of which models you've fine-tuned and their base model sources
|
||||
|
||||
### 5. GPU/Compute Safety
|
||||
|
||||
- Run on isolated machines if training on sensitive data
|
||||
- Clear cache and temporary files after training
|
||||
- Don't share fine-tuned models containing sensitive information
|
||||
|
||||
## Known Vulnerabilities
|
||||
|
||||
We maintain a log of known security issues and their fixes. This will be updated as issues are discovered and resolved.
|
||||
|
||||
### Current Status
|
||||
|
||||
No known critical vulnerabilities in current releases.
|
||||
|
||||
## Security Scanning
|
||||
|
||||
- All code is scanned with `ruff` for style and common issues
|
||||
- Dependencies are regularly updated to patch known CVEs
|
||||
- GitHub's dependency scanning alerts us to vulnerable dependencies
|
||||
- We use GitHub Actions CI/CD for continuous integration
|
||||
|
||||
## Dependency Updates
|
||||
|
||||
We actively monitor and update dependencies:
|
||||
|
||||
- Major dependency updates: Tested in PR before merging
|
||||
- Security patches: Applied immediately and released as patch versions
|
||||
- Deprecated dependencies: Replaced proactively
|
||||
|
||||
## Coming Soon
|
||||
|
||||
- [x] Automated dependency scanning
|
||||
- [ ] SBOM (Software Bill of Materials) for each release
|
||||
- [ ] Third-party security audit (after 1.0.0 release)
|
||||
|
||||
## Questions?
|
||||
|
||||
If you have security questions (not vulnerability reports) or need clarification:
|
||||
|
||||
- Open a GitHub Discussion tagged `security`
|
||||
- Email us at support@soup-cli.dev (non-vulnerability inquiries)
|
||||
- Check our [CONTRIBUTING.md](CONTRIBUTING.md) for general support
|
||||
|
||||
## License
|
||||
|
||||
This Security Policy is provided under the MIT license, same as the Soup project.
|
||||
|
||||
---
|
||||
|
||||
**Last Updated**: March 2026
|
||||
|
||||
For the latest version of this policy, visit: https://github.com/MakazhanAlpamys/Soup/blob/main/SECURITY.md
|
||||
|
|
@ -0,0 +1,278 @@
|
|||
# Soup Examples
|
||||
|
||||
Real-world configuration examples and sample datasets to get you running quickly.
|
||||
|
||||
## Quick Start with Examples
|
||||
|
||||
### 1. Basic SFT (Supervised Fine-Tuning)
|
||||
|
||||
Fine-tune TinyLlama on a small instruction-following dataset:
|
||||
|
||||
```bash
|
||||
soup train --config examples/configs/sft_basic.yaml
|
||||
```
|
||||
|
||||
**What it does:**
|
||||
- Trains TinyLlama-1.1B for 1 epoch
|
||||
- Uses LoRA for efficient memory usage
|
||||
- Outputs to `./output_sft_basic/`
|
||||
- Takes ~2-3 minutes on a consumer GPU
|
||||
|
||||
### 2. Chat Assistant (DPO)
|
||||
|
||||
Train a chat model with preference learning:
|
||||
|
||||
```bash
|
||||
soup train --config examples/configs/dpo_chat.yaml
|
||||
```
|
||||
|
||||
**What it does:**
|
||||
- Uses Llama 2 7B base model
|
||||
- Trains with DPO (Direct Preference Optimization) on chat preferences
|
||||
- Better alignment than SFT alone
|
||||
- Outputs to `./output_dpo_chat/`
|
||||
|
||||
### 3. Reasoning Model (GRPO)
|
||||
|
||||
Fine-tune a reasoning model with step-by-step answer verification:
|
||||
|
||||
```bash
|
||||
soup train --config examples/configs/grpo_reasoning.yaml
|
||||
```
|
||||
|
||||
**What it does:**
|
||||
- Trains on reasoning tasks (math, logic)
|
||||
- Uses GRPO (Group Relative Policy Optimization) to optimize for correctness
|
||||
- Generates multiple outputs per prompt and selects the best
|
||||
- Outputs to `./output_reasoning/`
|
||||
|
||||
### 4. Vision Model
|
||||
|
||||
Fine-tune LLaMA-Vision on image-caption pairs:
|
||||
|
||||
```bash
|
||||
soup train --config examples/configs/vision_llama.yaml
|
||||
```
|
||||
|
||||
**What it does:**
|
||||
- Trains LLaMA-3.2-Vision-90B on image-text data
|
||||
- Uses LLaVA format for images + text
|
||||
- Outputs to `./output_vision/`
|
||||
|
||||
### 5. Full RLHF Pipeline
|
||||
|
||||
Complete reinforcement learning from human feedback:
|
||||
|
||||
```bash
|
||||
# Step 1: Pre-train with SFT
|
||||
soup train --config examples/configs/rlhf_step1_sft.yaml
|
||||
|
||||
# Step 2: Train a reward model
|
||||
soup train --config examples/configs/rlhf_step2_reward.yaml
|
||||
|
||||
# Step 3: PPO with reward model
|
||||
soup train --config examples/configs/rlhf_step3_ppo.yaml
|
||||
```
|
||||
|
||||
## Dataset Formats
|
||||
|
||||
Datasets are included in JSONL format. Soup auto-detects and normalizes:
|
||||
|
||||
- **Alpaca**: `instruction`, `input`, `output` fields
|
||||
- **ShareGPT**: `conversations` with `from`/`value` fields
|
||||
- **ChatML**: OpenAI-style `messages` with `role`/`content`
|
||||
- **LLaVA**: Vision format with `image` + `conversations`
|
||||
|
||||
### Example: Inspect a Dataset
|
||||
|
||||
```bash
|
||||
soup data inspect examples/data/alpaca_tiny.jsonl
|
||||
```
|
||||
|
||||
Output:
|
||||
```
|
||||
📊 Dataset Statistics
|
||||
|
||||
Format detected: alpaca
|
||||
Total entries: 50
|
||||
Sample 1:
|
||||
instruction: "Identify the odd one out"
|
||||
input: "twitter, instagram, skype"
|
||||
output: "skype"
|
||||
```
|
||||
|
||||
### Example: Convert Between Formats
|
||||
|
||||
```bash
|
||||
# Convert Alpaca to ChatML
|
||||
soup data convert examples/data/alpaca_tiny.jsonl \
|
||||
--from alpaca --to chatml \
|
||||
--output alpaca_as_chatml.jsonl
|
||||
```
|
||||
|
||||
## Directory Structure
|
||||
|
||||
```
|
||||
examples/
|
||||
configs/ # YAML configuration files
|
||||
sft_basic.yaml
|
||||
dpo_chat.yaml
|
||||
grpo_reasoning.yaml
|
||||
vision_llama.yaml
|
||||
rlhf_step1_sft.yaml
|
||||
rlhf_step2_reward.yaml
|
||||
rlhf_step3_ppo.yaml
|
||||
|
||||
data/ # Sample datasets (JSONL)
|
||||
alpaca_tiny.jsonl
|
||||
chat_multichat.jsonl
|
||||
reasoning_math.jsonl
|
||||
vision_images.tar.gz (with image files)
|
||||
```
|
||||
|
||||
## Using Your Own Data
|
||||
|
||||
1. **Prepare data** in one of the supported formats
|
||||
2. **Update the config** with your data path:
|
||||
|
||||
```yaml
|
||||
data:
|
||||
path: /path/to/your/data.jsonl
|
||||
format: alpaca # or sharegpt, chatml, llava
|
||||
```
|
||||
|
||||
3. **Run training**:
|
||||
|
||||
```bash
|
||||
soup train --config your_config.yaml
|
||||
```
|
||||
|
||||
## Tips & Tricks
|
||||
|
||||
### Save Space: Use Quantization
|
||||
|
||||
Add quantization to reduce model size:
|
||||
|
||||
```yaml
|
||||
quantization: int8 # Reduces memory by 4x
|
||||
```
|
||||
|
||||
### Speed Up Training: Use Unsloth Backend
|
||||
|
||||
Unsloth is 2-5x faster training:
|
||||
|
||||
```bash
|
||||
pip install 'soup-cli[fast]'
|
||||
```
|
||||
|
||||
Then in your config:
|
||||
|
||||
```yaml
|
||||
backend: unsloth
|
||||
```
|
||||
|
||||
### Monitor Training: Use Weights & Biases
|
||||
|
||||
Enable W&B logging:
|
||||
|
||||
```bash
|
||||
pip install wandb
|
||||
soup train --config your_config.yaml --wandb
|
||||
```
|
||||
|
||||
### Export for Inference: Convert to GGUF
|
||||
|
||||
After training, convert for Ollama/llama.cpp:
|
||||
|
||||
```bash
|
||||
soup export output_sft_basic/ --output model.gguf --quant q8_0
|
||||
```
|
||||
|
||||
Then use with Ollama:
|
||||
|
||||
```bash
|
||||
ollama create my-model -f Ollama.modelfile
|
||||
```
|
||||
|
||||
### Merge LoRA Adapter
|
||||
|
||||
Merge your LoRA adapter into a standalone model:
|
||||
|
||||
```bash
|
||||
soup merge output_sft_basic/ --output merged_model/
|
||||
```
|
||||
|
||||
## Common Issues
|
||||
|
||||
### "CUDA out of memory"
|
||||
|
||||
- Reduce `batch_size` in config
|
||||
- Enable quantization: `quantization: int8`
|
||||
- Use smaller model: Mistral-7B instead of Llama-70B
|
||||
|
||||
### "Dataset not found"
|
||||
|
||||
- Check file path in config (use absolute path if unsure)
|
||||
- Verify format is correct: `soup data inspect your_data.jsonl`
|
||||
|
||||
### "Model not found on Hugging Face"
|
||||
|
||||
- Check model ID spelling
|
||||
- Ensure you have HuggingFace token: `huggingface-cli login`
|
||||
- Or use a different model that's publicly available
|
||||
|
||||
## Creating Your Own Configs
|
||||
|
||||
### Minimal Config Template
|
||||
|
||||
```yaml
|
||||
model: tinyllama-1.1b
|
||||
data:
|
||||
path: ./your_data.jsonl
|
||||
format: alpaca
|
||||
task: sft
|
||||
lora_r: 16
|
||||
lora_alpha: 32
|
||||
batch_size: 32
|
||||
num_epochs: 3
|
||||
learning_rate: 5e-4
|
||||
output_dir: ./output/
|
||||
```
|
||||
|
||||
### Advanced Config Template
|
||||
|
||||
```yaml
|
||||
model: llama-2-7b
|
||||
data:
|
||||
path: ./dataset.jsonl
|
||||
format: sharegpt
|
||||
task: dpo
|
||||
backend: unsloth
|
||||
quantization: int8
|
||||
lora_r: 64
|
||||
lora_alpha: 128
|
||||
lora_dropout: 0.05
|
||||
batch_size: 16
|
||||
gradient_accumulation_steps: 4
|
||||
num_epochs: 2
|
||||
learning_rate: 1e-4
|
||||
warmup_ratio: 0.1
|
||||
max_seq_length: 2048
|
||||
output_dir: ./output_advanced/
|
||||
```
|
||||
|
||||
See [config schema documentation](../CLAUDE.md#config-system) for all available options.
|
||||
|
||||
## Learn More
|
||||
|
||||
- **README**: [Main documentation](../README.md)
|
||||
- **CONTRIBUTING**: [How to contribute](../CONTRIBUTING.md)
|
||||
- **CLAUDE.md**: [Architecture and detailed docs](../CLAUDE.md)
|
||||
|
||||
## Questions?
|
||||
|
||||
- Check the [GitHub Discussions](https://github.com/MakazhanAlpamys/Soup/discussions)
|
||||
- Open an [Issue](https://github.com/MakazhanAlpamys/Soup/issues)
|
||||
- Read [SECURITY.md](../SECURITY.md) for security questions
|
||||
|
||||
Happy training! 🍲
|
||||
|
|
@ -0,0 +1,35 @@
|
|||
# DPO Chat Example
|
||||
# Train a chat model with Direct Preference Optimization
|
||||
# Uses Llama2-7B and preference data (chosen vs rejected)
|
||||
|
||||
model: meta-llama/Llama-2-7b-chat-hf
|
||||
data:
|
||||
path: examples/data/chat_preferences.jsonl
|
||||
format: sharegpt
|
||||
task: dpo
|
||||
backend: transformers
|
||||
quantization: int8
|
||||
lora_r: 64
|
||||
lora_alpha: 128
|
||||
lora_dropout: 0.05
|
||||
lora_target_modules:
|
||||
- q_proj
|
||||
- v_proj
|
||||
- k_proj
|
||||
- out_proj
|
||||
batch_size: 8
|
||||
gradient_accumulation_steps: 2
|
||||
num_epochs: 2
|
||||
learning_rate: 5e-4
|
||||
lr_scheduler_type: cosine
|
||||
warmup_ratio: 0.1
|
||||
weight_decay: 0.01
|
||||
max_seq_length: 2048
|
||||
output_dir: ./output_dpo_chat/
|
||||
seed: 42
|
||||
logging_steps: 10
|
||||
save_steps: 100
|
||||
eval_steps: 100
|
||||
eval_strategy: steps
|
||||
load_best_model_at_end: true
|
||||
dpo_beta: 0.1
|
||||
|
|
@ -0,0 +1,34 @@
|
|||
# GRPO Reasoning Example
|
||||
# Fine-tune a reasoning model (math, logic, step-by-step)
|
||||
# Uses Group Relative Policy Optimization to select best outputs
|
||||
|
||||
model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
|
||||
data:
|
||||
path: examples/data/reasoning_math.jsonl
|
||||
format: alpaca
|
||||
task: grpo
|
||||
backend: transformers
|
||||
quantization: null
|
||||
lora_r: 64
|
||||
lora_alpha: 128
|
||||
lora_dropout: 0.05
|
||||
lora_target_modules:
|
||||
- q_proj
|
||||
- v_proj
|
||||
batch_size: 8
|
||||
gradient_accumulation_steps: 2
|
||||
num_epochs: 2
|
||||
learning_rate: 1e-4
|
||||
lr_scheduler_type: cosine
|
||||
warmup_ratio: 0.1
|
||||
weight_decay: 0.01
|
||||
max_seq_length: 2048
|
||||
output_dir: ./output_reasoning/
|
||||
seed: 42
|
||||
grpo_beta: 0.05
|
||||
num_generations: 4
|
||||
reward_fn: accuracy # built-in reward function
|
||||
logging_steps: 10
|
||||
save_steps: 50
|
||||
eval_steps: 50
|
||||
eval_strategy: steps
|
||||
|
|
@ -0,0 +1,30 @@
|
|||
# RLHF Step 1: Supervised Fine-Tuning (SFT)
|
||||
# Pre-train the model before reward model training
|
||||
# Use high-quality examples
|
||||
|
||||
model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
|
||||
data:
|
||||
path: examples/data/alpaca_tiny.jsonl
|
||||
format: alpaca
|
||||
task: sft
|
||||
backend: transformers
|
||||
quantization: null
|
||||
lora_r: 64
|
||||
lora_alpha: 128
|
||||
lora_dropout: 0.05
|
||||
lora_target_modules:
|
||||
- q_proj
|
||||
- v_proj
|
||||
batch_size: 16
|
||||
gradient_accumulation_steps: 1
|
||||
num_epochs: 1
|
||||
learning_rate: 5e-4
|
||||
lr_scheduler_type: linear
|
||||
warmup_ratio: 0.05
|
||||
weight_decay: 0.0
|
||||
max_seq_length: 2048
|
||||
output_dir: ./output_rlhf_sft/
|
||||
seed: 42
|
||||
logging_steps: 10
|
||||
save_steps: 50
|
||||
save_strategy: steps
|
||||
|
|
@ -0,0 +1,24 @@
|
|||
# RLHF Step 2: Train Reward Model
|
||||
# Learn to score responses: is this output good or bad?
|
||||
# Uses preference pairs (chosen vs rejected)
|
||||
|
||||
model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
|
||||
data:
|
||||
path: examples/data/chat_preferences.jsonl # must have chosen/rejected
|
||||
format: sharegpt
|
||||
task: reward_model
|
||||
backend: transformers
|
||||
quantization: null
|
||||
batch_size: 8
|
||||
gradient_accumulation_steps: 2
|
||||
num_epochs: 2
|
||||
learning_rate: 1e-4
|
||||
lr_scheduler_type: cosine
|
||||
warmup_ratio: 0.1
|
||||
weight_decay: 0.01
|
||||
max_seq_length: 2048
|
||||
output_dir: ./output_reward_model/
|
||||
seed: 42
|
||||
logging_steps: 10
|
||||
save_steps: 50
|
||||
save_strategy: steps
|
||||
|
|
@ -0,0 +1,33 @@
|
|||
# RLHF Step 3: PPO Training
|
||||
# Optimize with reinforcement learning using the reward model
|
||||
# Generates outputs and ranks them with the reward model
|
||||
|
||||
model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
|
||||
data:
|
||||
path: examples/data/alpaca_tiny.jsonl
|
||||
format: alpaca
|
||||
task: ppo
|
||||
backend: transformers
|
||||
quantization: null
|
||||
lora_r: 64
|
||||
lora_alpha: 128
|
||||
lora_dropout: 0.05
|
||||
lora_target_modules:
|
||||
- q_proj
|
||||
- v_proj
|
||||
batch_size: 8
|
||||
gradient_accumulation_steps: 2
|
||||
num_epochs: 2
|
||||
learning_rate: 1e-5
|
||||
lr_scheduler_type: cosine
|
||||
warmup_ratio: 0.1
|
||||
weight_decay: 0.01
|
||||
max_seq_length: 2048
|
||||
output_dir: ./output_ppo/
|
||||
seed: 42
|
||||
ppo_epochs: 4
|
||||
ppo_clip_ratio: 0.2
|
||||
ppo_kl_penalty: 0.05
|
||||
reward_model: ./output_reward_model/
|
||||
logging_steps: 10
|
||||
save_steps: 50
|
||||
|
|
@ -0,0 +1,32 @@
|
|||
# SFT Basic Example
|
||||
# Fine-tune TinyLlama-1.1B on instruction-following data
|
||||
# Quick to train (2-3 minutes on consumer GPU) — perfect for testing
|
||||
|
||||
model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
|
||||
data:
|
||||
path: examples/data/alpaca_tiny.jsonl
|
||||
format: alpaca
|
||||
task: sft
|
||||
backend: transformers
|
||||
quantization: null
|
||||
lora_r: 16
|
||||
lora_alpha: 32
|
||||
lora_dropout: 0.05
|
||||
lora_target_modules:
|
||||
- q_proj
|
||||
- v_proj
|
||||
batch_size: 16
|
||||
gradient_accumulation_steps: 1
|
||||
num_epochs: 1
|
||||
learning_rate: 5e-4
|
||||
lr_scheduler_type: linear
|
||||
warmup_ratio: 0.05
|
||||
weight_decay: 0.0
|
||||
max_seq_length: 512
|
||||
output_dir: ./output_sft_basic/
|
||||
seed: 42
|
||||
logging_steps: 10
|
||||
save_steps: 50
|
||||
eval_steps: 50
|
||||
eval_strategy: steps
|
||||
load_best_model_at_end: false
|
||||
|
|
@ -0,0 +1,33 @@
|
|||
# Vision Fine-tuning Example
|
||||
# Train LLaMA-Vision on image-caption pairs
|
||||
# Uses LLaVA format (image + conversation)
|
||||
|
||||
model: llama-vision-13b # LLaMA-3.2-Vision-90B or similar
|
||||
data:
|
||||
path: examples/data/vision_dataset.jsonl
|
||||
format: llava
|
||||
image_dir: examples/data/images/
|
||||
task: sft
|
||||
modality: vision
|
||||
backend: transformers
|
||||
quantization: int8
|
||||
lora_r: 128
|
||||
lora_alpha: 256
|
||||
lora_dropout: 0.1
|
||||
lora_target_modules:
|
||||
- q_proj
|
||||
- v_proj
|
||||
- k_proj
|
||||
- out_proj
|
||||
batch_size: 4
|
||||
gradient_accumulation_steps: 4
|
||||
num_epochs: 2
|
||||
learning_rate: 1e-4
|
||||
lr_scheduler_type: cosine
|
||||
warmup_ratio: 0.1
|
||||
weight_decay: 0.01
|
||||
max_seq_length: 4096
|
||||
output_dir: ./output_vision/
|
||||
seed: 42
|
||||
logging_steps: 10
|
||||
save_steps: 50
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
{"instruction": "Classify the sentiment of this sentence.", "input": "I love this product, it's amazing!", "output": "Positive"}
|
||||
{"instruction": "Translate to Spanish.", "input": "Hello, how are you?", "output": "Hola, ¿cómo estás?"}
|
||||
{"instruction": "Write a haiku about nature.", "input": "", "output": "Green leaves dance in wind\nWhispering ancient forest\nPeace in silence dwells"}
|
||||
{"instruction": "Explain quantum computing in simple terms.", "input": "", "output": "Quantum computers use quantum bits (qubits) instead of normal bits. Unlike regular bits which are 0 or 1, qubits can be both at once (superposition). This allows quantum computers to solve certain problems much faster than regular computers."}
|
||||
{"instruction": "Fix the grammar in this sentence.", "input": "She go to the store yesterday.", "output": "She went to the store yesterday."}
|
||||
{"instruction": "List 3 benefits of exercise.", "input": "", "output": "1. Improves cardiovascular health and strengthens the heart.\n2. Helps maintain a healthy weight and builds muscle.\n3. Reduces stress and improves mental health and mood."}
|
||||
{"instruction": "What is the capital of France?", "input": "", "output": "The capital of France is Paris."}
|
||||
{"instruction": "Summarize this text in one sentence.", "input": "Machine learning is a subset of artificial intelligence that enables computers to learn from data without being explicitly programmed. It uses algorithms to identify patterns in data and improve through experience.", "output": "Machine learning allows computers to learn patterns from data without explicit programming."}
|
||||
{"instruction": "Create a Python function to add two numbers.", "input": "", "output": "def add_numbers(a, b):\n \"\"\"Add two numbers and return the result.\"\"\"\n return a + b\n\n# Example usage\nresult = add_numbers(5, 3)\nprint(result) # Output: 8"}
|
||||
{"instruction": "Identify the main idea of this paragraph.", "input": "Climate change is caused by increased greenhouse gases in the atmosphere, primarily from human activities like burning fossil fuels. This leads to global warming, which affects weather patterns, sea levels, and ecosystems.", "output": "Greenhouse gases from human activities cause climate change and have significant environmental impacts."}
|
||||
|
|
@ -0,0 +1,5 @@
|
|||
{"conversations": [{"from": "user", "value": "What is machine learning?"}, {"from": "assistant", "value": "Machine learning is a field of artificial intelligence where systems learn from data automatically. It uses algorithms to identify patterns and improve performance through experience, without being explicitly programmed for every task."}], "chosen": true}
|
||||
{"conversations": [{"from": "user", "value": "How do I learn Python?"}, {"from": "assistant", "value": "Here are effective ways to learn Python:\n1. Start with fundamentals: variables, loops, functions\n2. Practice with small projects\n3. Use resources like Python.org docs, tutorials\n4. Build projects to apply what you learn\n5. Join communities for support"}], "chosen": true}
|
||||
{"conversations": [{"from": "user", "value": "What's the difference between AI and ML?"}, {"from": "assistant", "value": "AI (Artificial Intelligence) is the broader field of creating intelligent systems. ML (Machine Learning) is a subset of AI focused on learning from data. All machine learning is AI, but not all AI uses machine learning."}], "chosen": true}
|
||||
{"conversations": [{"from": "user", "value": "Explain neural networks simply."}, {"from": "assistant", "value": "Neural networks are inspired by the human brain. They consist of layers of connected nodes (neurons) that process information. Each connection has a weight that adjusts during training. This allows the network to learn complex patterns in data."}], "chosen": true}
|
||||
{"conversations": [{"from": "user", "value": "How should I structure a machine learning project?"}, {"from": "assistant", "value": "Follow this structure:\n1. Define the problem and gather data\n2. Explore and clean the data\n3. Choose and train a model\n4. Evaluate performance\n5. Iterate and improve\n6. Deploy to production\n7. Monitor and maintain"}], "chosen": true}
|
||||
|
|
@ -0,0 +1,5 @@
|
|||
{"instruction": "Solve this math problem: 7 + 5 * 2 =", "input": "", "output": "To solve 7 + 5 * 2, I need to follow the order of operations (PEMDAS).\n\nStep 1: Multiply first (5 * 2)\n5 * 2 = 10\n\nStep 2: Add (7 + 10)\n7 + 10 = 17\n\nAnswer: 17"}
|
||||
{"instruction": "Explain the Pythagorean theorem and solve an example.", "input": "", "output": "The Pythagorean theorem states that in a right triangle, a² + b² = c², where c is the hypotenuse (longest side).\n\nExample: If a = 3 and b = 4, find c\n\nStep 1: Substitute into formula\n3² + 4² = c²\n\nStep 2: Calculate squares\n9 + 16 = c²\n\nStep 3: Add\n25 = c²\n\nStep 4: Take square root\nc = √25 = 5\n\nAnswer: c = 5"}
|
||||
{"instruction": "Find the GCD of 48 and 18.", "input": "", "output": "To find the GCD (Greatest Common Divisor) of 48 and 18:\n\nMethod: Euclidean algorithm\n\nStep 1: 48 = 18 × 2 + 12\nStep 2: 18 = 12 × 1 + 6\nStep 3: 12 = 6 × 2 + 0\n\nWhen remainder is 0, the GCD is the last non-zero remainder.\n\nGCD(48, 18) = 6\n\nAnswer: 6"}
|
||||
{"instruction": "Solve: x + 5 = 12", "input": "", "output": "This is a simple linear equation.\n\nGiven: x + 5 = 12\n\nStep 1: Subtract 5 from both sides\nx + 5 - 5 = 12 - 5\n\nStep 2: Simplify\nx = 7\n\nVerification: 7 + 5 = 12 ✓\n\nAnswer: x = 7"}
|
||||
{"instruction": "What is 15% of 200?", "input": "", "output": "To find 15% of 200:\n\nMethod: Multiply by the percentage as a decimal\n15% = 15/100 = 0.15\n\n0.15 × 200 = 30\n\nAnswer: 30"}
|
||||
|
|
@ -4,7 +4,7 @@ build-backend = "hatchling.build"
|
|||
|
||||
[project]
|
||||
name = "soup-cli"
|
||||
version = "0.9.0"
|
||||
version = "0.10.0"
|
||||
description = "Fine-tune LLMs in one command. No SSH, no config hell."
|
||||
readme = "README.md"
|
||||
license = "MIT"
|
||||
|
|
|
|||
|
|
@ -1,3 +1,3 @@
|
|||
"""Soup CLI — Fine-tune LLMs in one command."""
|
||||
|
||||
__version__ = "0.9.0"
|
||||
__version__ = "0.10.0"
|
||||
|
|
|
|||
Loading…
Reference in New Issue