v0.5.0

.env.example

@@ -16,3 +16,6 @@ OPENAI_API_KEY=sk-your-api-key-here
 # vLLM / Local server
 # OPENAI_BASE_URL=http://localhost:8000/v1
 # OPENAI_API_KEY=dummy-key
+
+# Optional: Hugging Face token to push generated datasets
+# HF_TOKEN=your-huggingface-token-here

CHANGELOG.md

@@ -8,6 +8,19 @@ The format is based on Keep a Changelog, and this project adheres to Semantic Ve
 
 Nothing yet.
 
+## [0.5.0] - 2025-01-11
+### Added
+- Hugging Face Hub integration for direct dataset uploads
+  - `push_to_hub()` function in new `hf_hub` module to upload datasets to HF Hub
+  - Uploads JSONL files (train.jsonl, val.jsonl), manifest.json, and auto-generated README.md
+  - CLI flags: `--push-to-hub`, `--repo-id`, `--hf-token`, `--private`
+  - Support for both public and private repositories
+  - Auto-generated dataset cards with dataset statistics, model info, usage examples, and citation
+- Optional dependency: `huggingface_hub>=0.20.0` (install with `pip install toolsgen[hf]`)
+- Example in `examples/hf_hub_upload/` with dotenv configuration
+- Test suite for HF Hub functionality in `tests/test_hf_hub.py`
+- `push_to_hub` exported from main `toolsgen` package for easier imports
+
 ## [0.4.0] - 2025-01-10
 ### Added
 - Quality tagging system for generated records

README.md

@@ -76,16 +76,26 @@ toolsgen generate \
   --num 500 \
   --workers 6 \
   --worker-batch-size 4
+
+# Generate and push directly to Hugging Face Hub
+export HF_TOKEN="your-hf-token-here"
+toolsgen generate \
+  --tools tools.json \
+  --out output_dir \
+  --num 100 \
+  --push-to-hub \
+  --repo-id username/dataset-name
 ```
 
 ### Python API Usage
 
 ```python
-import os
 from pathlib import Path
+from dotenv import load_dotenv
+
 from toolsgen.core import GenerationConfig, ModelConfig, generate_dataset
 
-os.environ["OPENAI_API_KEY"] = "your-api-key-here"
+load_dotenv()  # Load from .env file
 
 # Configuration
 tools_path = Path("tools.json")
@@ -119,6 +129,50 @@ print(f"Generated {manifest['num_generated']}/{manifest['num_requested']} record
 print(f"Failed: {manifest['num_failed']} attempts")
 ```
 
+### Push to Hugging Face Hub
+
+```python
+from pathlib import Path
+from dotenv import load_dotenv
+
+from toolsgen import GenerationConfig, ModelConfig, generate_dataset, push_to_hub
+
+load_dotenv()  # Load from .env file
+
+tools_path = Path("tools.json")
+output_dir = Path("output")
+
+gen_config = GenerationConfig(
+    num_samples=100,
+    strategy="random",
+    seed=42,
+    train_split=0.9,
+)
+
+model_config = ModelConfig(
+    model="gpt-4o-mini",
+    temperature=0.7,
+)
+
+# Generate dataset
+manifest = generate_dataset(
+    output_dir=output_dir,
+    gen_config=gen_config,
+    model_config=model_config,
+    tools_path=tools_path,
+)
+
+# Push to Hub
+hub_info = push_to_hub(
+    output_dir=output_dir,
+    repo_id="username/dataset-name",
+    private=False,
+)
+
+print(f"Generated: {manifest['num_generated']} records")
+print(f"Repository: {hub_info['repo_url']}")
+```
+
 See `examples/` directory for complete working examples.
 
 **Note**: The examples in `examples/` use `python-dotenv` for convenience (load API keys from `.env` file). Install it with `pip install python-dotenv` if you want to use this approach.
@@ -228,7 +282,7 @@ For detailed information about the system architecture, pipeline, and core compo
 - [ ] Custom prompt template system
 - [x] Parallel generation with multiprocessing
 - [ ] Additional sampling strategies (coverage-based, difficulty-based)
-- [ ] Integration with Hugging Face Hub for direct dataset uploads
+- [x] Integration with Hugging Face Hub for direct dataset uploads
 - [ ] Support for more LLM providers (Anthropic, Cohere, etc.)
 - [ ] Web UI for dataset inspection and curation
 - [ ] Advanced filtering and deduplication

examples/hf_hub_upload/README.md

@@ -0,0 +1,76 @@
+# Hugging Face Hub Upload Example
+
+This example demonstrates how to generate a dataset and push it directly to Hugging Face Hub.
+
+## Prerequisites
+
+1. OpenAI API key
+2. Hugging Face account and token with write access
+
+## Setup
+
+```bash
+# Install dependencies
+pip install toolsgen huggingface_hub python-dotenv
+
+# Create .env file from example
+cp .env.example .env
+
+# Edit .env and add your API keys
+# OPENAI_API_KEY=your-openai-api-key
+# HF_TOKEN=your-huggingface-token
+```
+
+## Usage
+
+### Python API
+
+```python
+python example.py
+```
+
+Make sure to update the `repo_id` in the script to your own repository name.
+
+### CLI
+
+```bash
+toolsgen generate \
+  --tools ../basic/tools.json \
+  --out output \
+  --num 50 \
+  --push-to-hub \
+  --repo-id your-username/your-dataset-name
+```
+
+## What Gets Uploaded
+
+The following files are automatically uploaded to your HF Hub repository:
+
+- `train.jsonl` - Training dataset
+- `val.jsonl` - Validation dataset (if train_split < 1.0)
+- `manifest.json` - Generation metadata
+- `README.md` - Auto-generated dataset card
+
+## Repository Visibility
+
+By default, repositories are public. To create a private repository:
+
+**Python API:**
+```python
+hub_info = push_to_hub(
+    output_dir=output_dir,
+    repo_id="username/dataset-name",
+    private=True,
+)
+```
+
+**CLI:**
+```bash
+toolsgen generate ... --push-to-hub --private
+```
+
+## Notes
+
+- The HF token can be provided via `--hf-token` flag or `HF_TOKEN` environment variable
+- If a repository already exists, it will be updated with new files
+- A dataset card (README.md) is automatically generated if not present

examples/hf_hub_upload/example.py

@@ -0,0 +1,45 @@
+"""Example: Generate dataset and push to Hugging Face Hub."""
+
+from pathlib import Path
+
+from dotenv import load_dotenv
+
+from toolsgen import GenerationConfig, ModelConfig, generate_dataset, push_to_hub
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Configuration
+tools_path = Path(__file__).parent.parent / "basic" / "tools.json"
+output_dir = Path(__file__).parent / "output"
+
+gen_config = GenerationConfig(
+    num_samples=50,
+    strategy="random",
+    seed=42,
+    train_split=0.9,
+)
+
+model_config = ModelConfig(
+    model="gpt-4o-mini",
+    temperature=0.7,
+)
+
+# Generate dataset
+manifest = generate_dataset(
+    output_dir=output_dir,
+    gen_config=gen_config,
+    model_config=model_config,
+    tools_path=tools_path,
+)
+
+# Push to Hub
+hub_info = push_to_hub(
+    output_dir=output_dir,
+    repo_id="your-username/your-dataset-name",  # Change this!
+    private=False,
+)
+
+print("\n✓ Dataset generated and uploaded!")
+print(f"  Generated: {manifest['num_generated']} records")
+print(f"  Repository: {hub_info['repo_url']}")

pyproject.toml

@@ -13,7 +13,7 @@ toolsgen = ["prompts/*.txt"]
 
 [project]
 name = "toolsgen"
-version = "0.4.0"
+version = "0.5.0"
 description = "Generate tool-calling datasets from OpenAI-compatible tool specs"
 readme = "README.md"
 requires-python = ">=3.9"
@@ -40,6 +40,11 @@ dependencies = [
   "tqdm>=4.66.0",
 ]
 
+[project.optional-dependencies]
+hf = [
+  "huggingface_hub>=0.20.0",
+]
+
 [project.urls]
 Homepage = "https://github.com/atasoglu/toolsgen"
 Repository = "https://github.com/atasoglu/toolsgen"

requirements-dev.txt

@@ -3,3 +3,4 @@ pytest-cov>=5.0.0
 pre-commit>=3.7.0
 ruff>=0.6.0
 python-dotenv>=1.0.0
+huggingface_hub>=0.20.0

src/toolsgen/__init__.py

@@ -14,6 +14,7 @@
     load_tool_specs,
     write_dataset_jsonl,
 )
+from .hf_hub import push_to_hub
 from .judge import JudgeResponse, judge_tool_calls
 from .problem_generator import generate_problem
 from .tool_caller import generate_tool_calls
@@ -55,6 +56,8 @@
     "generate_dataset",
     "load_tool_specs",
     "write_dataset_jsonl",
+    # HF Hub
+    "push_to_hub",
     # Judge
     "JudgeResponse",
     "judge_tool_calls",

src/toolsgen/cli.py

@@ -168,6 +168,28 @@ def create_parser() -> argparse.ArgumentParser:
         help="Temperature for judging (defaults to --temperature)",
     )
 
+    # Hugging Face Hub options
+    gen_parser.add_argument(
+        "--push-to-hub",
+        action="store_true",
+        help="Push dataset to Hugging Face Hub after generation",
+    )
+    gen_parser.add_argument(
+        "--repo-id",
+        default=None,
+        help="HF Hub repository ID (e.g., 'username/dataset-name')",
+    )
+    gen_parser.add_argument(
+        "--hf-token",
+        default=None,
+        help="HF API token (defaults to HF_TOKEN env var)",
+    )
+    gen_parser.add_argument(
+        "--private",
+        action="store_true",
+        help="Create private repository on HF Hub",
+    )
+
     return parser
 
 
@@ -275,9 +297,15 @@ def cmd_generate(args: argparse.Namespace) -> None:
             max_tokens=args.max_tokens,
         )
 
+    # Validate HF Hub options
+    if args.push_to_hub and not args.repo_id:
+        print("Error: --repo-id is required when using --push-to-hub", file=sys.stderr)
+        sys.exit(1)
+
     # Generate dataset
     try:
         print(f"Generating {args.num} samples using {args.model}...")
+
         manifest = generate_dataset(
             args.out, gen_config, model_config, tools_path=args.tools
         )
@@ -297,6 +325,20 @@ def cmd_generate(args: argparse.Namespace) -> None:
 
         print(f"  - Manifest: {args.out / 'manifest.json'}")
 
+        if args.push_to_hub:
+            from .hf_hub import push_to_hub
+
+            print("\nPushing to Hugging Face Hub...")
+            hub_info = push_to_hub(
+                output_dir=args.out,
+                repo_id=args.repo_id,
+                token=args.hf_token,
+                private=args.private,
+            )
+            print("✓ Pushed to Hugging Face Hub")
+            print(f"  - Repository: {hub_info['repo_url']}")
+            print(f"  - Files uploaded: {', '.join(hub_info['files_uploaded'])}")
+
     except ValueError as e:
         print(f"Error: {e}", file=sys.stderr)
         sys.exit(1)