{"id":34846907,"url":"https://github.com/always-further/deepfabric","last_synced_at":"2026-02-03T20:09:22.009Z","repository":{"id":259600648,"uuid":"878416567","full_name":"always-further/deepfabric","owner":"always-further","description":"Generate High-Quality Synthetics, Train, Measure, and Evaluate in a Single 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\u003cpicture\u003e\n    \u003csource media=\"(prefers-color-scheme: dark)\" srcset=\"./assets/logo-light.png\" /\u003e\n    \u003cimg alt=\"DeepFabric logo\" src=\"./assets/logo-light.png\" style=\"width:40%;max-width:40%;height:auto;display:block;margin:0 auto;\" /\u003e\n  \u003c/picture\u003e\n  \u003ch3\u003eTraining Model Behavior in Agentic Systems\u003c/h3\u003e\n\n  \u003c!-- CTA Buttons --\u003e\n  \u003cp\u003e\n    \u003ca href=\"https://github.com/always-further/deepfabric/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22\"\u003e\n      \u003cimg src=\"https://img.shields.io/badge/Contribute-Good%20First%20Issues-green?style=for-the-badge\u0026logo=github\" alt=\"Good First Issues\"/\u003e\n    \u003c/a\u003e\n    \u0026nbsp;\n    \u003ca href=\"https://discord.gg/pPcjYzGvbS\"\u003e\n      \u003cimg src=\"https://img.shields.io/badge/Chat-Join%20Discord-7289da?style=for-the-badge\u0026logo=discord\u0026logoColor=white\" alt=\"Join Discord\"/\u003e\n    \u003c/a\u003e\n  \u003c/p\u003e\n\n  \u003c!-- Badges --\u003e\n  \u003cp\u003e\n    \u003ca href=\"https://opensource.org/licenses/Apache-2.0\"\u003e\n      \u003cimg src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" alt=\"License\"/\u003e\n    \u003c/a\u003e\n    \u003ca href=\"https://github.com/always-further/deepfabric/actions/workflows/test.yml\"\u003e\n      \u003cimg src=\"https://github.com/always-further/deepfabric/actions/workflows/test.yml/badge.svg\" alt=\"CI Status\"/\u003e\n    \u003c/a\u003e\n    \u003ca href=\"https://pypi.org/project/deepfabric/\"\u003e\n      \u003cimg src=\"https://img.shields.io/pypi/v/deepfabric.svg\" alt=\"PyPI Version\"/\u003e\n    \u003c/a\u003e\n    \u003ca href=\"https://pepy.tech/project/deepfabric\"\u003e\n      \u003cimg src=\"https://static.pepy.tech/badge/deepfabric\" alt=\"Downloads\"/\u003e\n    \u003c/a\u003e\n    \u003ca href=\"https://discord.gg/pPcjYzGvbS\"\u003e\n      \u003cimg src=\"https://img.shields.io/discord/1384081906773131274?color=7289da\u0026label=Discord\u0026logo=discord\u0026logoColor=white\" alt=\"Discord\"/\u003e\n    \u003c/a\u003e\n    \u003ca href=\"https://www.reddit.com/r/deepfabric/\"\u003e\n      \u003cimg src=\"https://img.shields.io/badge/Reddit-r%2Fdeepfabric-FF4500?logo=reddit\u0026logoColor=white\" alt=\"Reddit\"/\u003e\n    \u003c/a\u003e\n  \u003c/p\u003e\n\u003c/div\u003e\n\n**DeepFabric** generates synthetic training data for language models and agent evaluations. By combining reasoning traces with tool-calling patterns, it creates high-quality, domain-specific datasets that teach models to think, plan, and act effectively, call tools correctly, and conform to strict schema structures.\n\nWhat sets DeepFabric apart from other dataset generation tools is its ability to ensure high diversity yet domain-anchored relevance through unique topic graph generation algorithms. This guides sample creation to cover all necessary subtopics while avoiding redundancy, which is where other tools often fall short, resulting in model overfit.\n\n\u003cimg src=\"/assets/df-demo.gif\" width=\"100%\" height=\"100%\"/\u003e\n\nConstrained decoding and response validation, along with real tool executions within isolated webassembly environments, ensure that generated samples strictly adhere to structured schema, variable constraints, and execution correctness, ensuring datasets have exact syntax and structure for use in model training pipelines. Tool definations can be either directly imported from MCP (Model Context Protocol) server schemas and automatically mocked, real life interfaces along with a standard set of common tools (`list_files()`, `'read_file()` etc)\n\nOnce your dataset is generated, it can be automatically uploaded to Hugging Face and directly imported into popular training frameworks like TRL, Unsloth, and Axolotl. \n\nPost-training, DeepFabric's built-in evaluation engine assesses model performance, whereby models prove their capabilities on unseen tasks derived from training splits—covering evaluation-only questions, answers, and tool traces.\n\n## Quickstart\n\nDeepFabric can be used in several ways, as a library, CLI tool, or via YAML configuration. Here's a quick example using the CLI:\n\n```bash\npip install deepfabric\n```\n\n```bash\nexport OPENAI_API_KEY=\"your-api-key\"\n\ndeepfabric generate \\\n  --topic-prompt \"Python programming fundamentals\" \\\n  --generation-system-prompt \"You are a Python expert\" \\\n  --mode graph \\\n  --depth 3 \\\n  --degree 3 \\\n  --num-samples 9 \\\n  --batch-size 3 \\\n  --provider openai \\\n  --model gpt-4o \\\n  --output-save-as dataset.jsonl\n```\n\nThis generates a topic graph and creates 27 unique nodes, then generates 27 training samples saved to `dataset.jsonl`, giving you 100% topic coverage.\n\n## Configuration\n\nDeepFabric also uses YAML configuration with three main sections and optional shared LLM defaults\n\n\u003e [!NOTE]  \n\u003e The following uses mocked tool execution, so will require a runing Spin service, which we provide in a docker image:\n```bash\ndocker run -d -p 3000:3000 ghcr.io/always-further/deepfabric/tools-sdk:latest`\n```\n\nSave the following as `config.yaml`:\n\n```yaml\n# Optional: Shared LLM defaults (inherited by topics and generation)\nllm:\n  provider: \"openai\"\n  model: \"gpt-4o\"\n  temperature: 0.7\n\n# TOPICS: Generate the topic tree/graph\ntopics:\n  prompt: \"Building production-ready REST APIs with Python\"\n  mode: tree                    # tree | graph\n  depth: 3\n  degree: 3\n  save_as: \"topics.jsonl\"\n  # Optional: Override shared LLM settings\n  llm:\n    model: \"gpt-4o-mini\"        # Use cheaper model for topics\n\n# GENERATION: Create training samples from topics\ngeneration:\n  system_prompt: |\n    You are an expert Python backend developer specializing in REST API design.\n    Create practical, production-ready code examples with clear explanations.\n    Include error handling, type hints, and follow PEP 8 conventions.\n    Use the following tools to read, write, and list files in the virtual filesystem:\n    - read_file\n    - write_file\n    - list_files\n\n  # Additional instructions for sample generation\n  instructions: |\n    Focus on real-world scenarios developers encounter daily when building REST APIs with Python.\n    Include both happy path and edge case handling.\n    Provide context on when and why to use specific patterns or libraries.\n    Ensure code is modular, testable, and maintainable.\n\n  # Agent mode is implicit when tools are configured\n  conversation:\n    type: cot      # basic | cot\n    reasoning_style: agent      # freetext | agent (for cot)\n\n  # Tool configuration (enables agent mode automatically)\n  tools:\n    spin_endpoint: \"http://localhost:3000\"  # Spin service for tool execution\n    components:                 # Map component name to tool names\n      builtin:                  # Routes to /vfs/execute\n        - read_file\n        - write_file\n        - list_files\n    max_per_query: 3            # Maximum tools per query\n    max_agent_steps: 5          # Max ReAct reasoning iterations\n\n  # Optional: Seed initial files into the spin before generation, used for tool calling\n    scenario_seed:\n      files:\n        \"Dockerfile\": |\n          FROM python:3.13\n          WORKDIR /usr/local/app\n\n          # Install the application dependencies\n          COPY requirements.txt ./\n          RUN pip install --no-cache-dir -r requirements.txt\n\n          # Copy in the source code\n          COPY src ./src\n          EXPOSE 8080\n\n          # Setup an app user so the container doesn't run as the root user\n          RUN useradd app\n          USER app\n\n          CMD [\"uvicorn\", \"app.main:app\", \"--host\", \"0.0.0.0\", \"--port\", \"8080\"]\n        \"main.py\": |\n          def greet(name):\n              return f\"Hello, {name}!\"\n\n          if __name__ == \"__main__\":\n              print(greet(\"World\"))\n        \"config.json\": |\n          {\n            \"version\": \"1.0.0\",\n            \"debug\": true,\n            \"max_retries\": 3\n          }\n\n  # Generation control and retry settings\n  max_retries: 3                # Retries for failed generations\n  sample_retries: 2             # Retries for validation failures\n  max_tokens: 2000              # Max tokens per generation\n\n  # Optional: Override shared LLM settings\n  llm:\n    temperature: 0.3            # Lower temp for consistent code\n\n# OUTPUT: Final dataset configuration\noutput:\n  # System prompt that goes INTO the training data\n  # This is what the trained model will see as its system message\n  system_prompt: |\n    You are a helpful Python programming assistant specialized in REST API\n    development. You provide clear, production-ready code with explanations.\n    Always consider security, error handling, and best practices.\n\n  include_system_message: true  # Whether to include system message in output\n  num_samples: 4                 # Total training samples to generate\n  batch_size: 3                 # Parallel generation batch size\n  save_as: \"api-dataset.jsonl\"\n\n Optional: Upload to Hugging Face\n huggingface:\n   repository: \"your-username/api-dataset-training-name\"\n   tags: [\"python\", \"programming\"]\n```\n\nRun generation by sourcing the `config.yaml`:\n\n```bash\ndeepfabric generate config.yaml\n```\n\n## Generate, Train, Evaluate\n\nDeepFabric returns standard HuggingFace datasets, making it easy to integrate with any training framework.\n\n### Colab Notebooks:\n\nA quick way of seeing DeepFabric in action is via our notebooks in the [notebooks/](./notebooks/) folder or on Google Colab:\n\n**Qwen4b Blender MCP**:\n\n[![Qwen4b Blender MCP](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1EG1V40v5xkJKLf6Ra6W4378vYqlZNVWqb)\n\n### 1. Generate Dataset\n\n```bash\ndeepfabric generate config.yaml --output-save-as dataset.jsonl\n```\n\nOr upload to HuggingFace Hub:\n\n```bash\ndeepfabric upload-hf dataset.jsonl --repo your-username/my-dataset\n```\n\n### 2. Load and Split for Training\n\n```python\nfrom datasets import load_dataset\nfrom transformers import AutoTokenizer\n\n# Load from Hub\ndataset = load_dataset(\"alwaysfurther/deepfabric-generic-tools\", split=\"train\")\n\n# Split into train/eval\nsplits = dataset.train_test_split(test_size=0.1, seed=42)\ntrain_ds = splits[\"train\"]\neval_ds = splits[\"test\"]\n\n# Format using your tokenizer\ntokenizer = AutoTokenizer.from_pretrained(\"Qwen/Qwen2.5-7B-Instruct\")\n\ndef format_example(example):\n    messages = [{k: v for k, v in msg.items() if v is not None}\n                for msg in example[\"messages\"]]\n    return {\"text\": tokenizer.apply_chat_template(messages, tokenize=False)}\n\nformatted_train = train_ds.map(format_example)\n```\n\n### 3. Train with TRL or Unsloth\n\n```python\nfrom trl import SFTTrainer, SFTConfig\n\ntrainer = SFTTrainer(\n    model=model,\n    tokenizer=tokenizer,\n    train_dataset=formatted_train,\n    args=SFTConfig(output_dir=\"./output\", num_train_epochs=3),\n)\ntrainer.train()\n```\n\n### 4. Evaluate Your Model\n\n```python\nfrom deepfabric.evaluation import Evaluator, EvaluatorConfig, InferenceConfig\n\nconfig = EvaluatorConfig(\n    inference_config=InferenceConfig(\n        model_path=\"./output/checkpoint-final\",  # Local path or HF Hub ID\n        backend=\"transformers\",\n    ),\n)\n\nevaluator = Evaluator(config)\nresults = evaluator.evaluate(dataset=eval_ds)  # Pass HF Dataset directly\n\nprint(f\"Tool Selection Accuracy: {results.metrics.tool_selection_accuracy:.2%}\")\nprint(f\"Parameter Accuracy: {results.metrics.parameter_accuracy:.2%}\")\nprint(f\"Overall Score: {results.metrics.overall_score:.2%}\")\n```\n\n## Evaluation\n\nDeepFabric provides a comprehensive evaluation system to measure how well your fine-tuned models perform on tool-calling tasks.\n\n### Basic Evaluation\n\n```python\nfrom datasets import load_dataset\nfrom deepfabric.evaluation import Evaluator, EvaluatorConfig, InferenceConfig\n\n# Load your evaluation dataset\ndataset = load_dataset(\"your-username/your-dataset\", split=\"test\")\n\n# Configure the evaluator\nconfig = EvaluatorConfig(\n    inference_config=InferenceConfig(\n        model_path=\"./output/checkpoint-final\",  # Local path or HF Hub ID\n        backend=\"transformers\",                   # \"transformers\" or \"ollama\"\n        temperature=0.1,                          # Low temp for deterministic outputs\n        max_tokens=2048,\n    ),\n    max_samples=100,           # Limit samples for quick testing (None for all)\n    save_predictions=True,     # Save individual predictions\n    output_path=\"eval_results.json\",\n)\n\n# Run evaluation\nevaluator = Evaluator(config)\nresults = evaluator.evaluate(dataset=dataset)\n\n# Print summary\nevaluator.print_summary(results.metrics)\n\n# Cleanup GPU memory\nevaluator.cleanup()\n```\n\n### Evaluation with LoRA Adapters\n\n```python\nfrom deepfabric.evaluation import Evaluator, EvaluatorConfig, InferenceConfig\n\nconfig = EvaluatorConfig(\n    inference_config=InferenceConfig(\n        model_path=\"Qwen/Qwen2.5-7B-Instruct\",    # Base model\n        adapter_path=\"./output/lora-adapter\",     # LoRA adapter path\n        backend=\"transformers\",\n        load_in_4bit=True,     # 4-bit quantization\n        max_seq_length=2048,\n    ),\n)\n\nevaluator = Evaluator(config)\nresults = evaluator.evaluate(dataset=eval_dataset)\n```\n\n### Understanding Evaluation Metrics\n\nThe evaluator computes several metrics for tool-calling tasks:\n\n```python\nresults = evaluator.evaluate(dataset=eval_dataset)\nmetrics = results.metrics\n\n# Core metrics\nprint(f\"Samples Evaluated: {metrics.samples_evaluated}\")\nprint(f\"Samples Processed: {metrics.samples_processed}\")\nprint(f\"Processing Errors: {metrics.processing_errors}\")\n\n# Tool-calling metrics\nprint(f\"Tool Selection Accuracy: {metrics.tool_selection_accuracy:.2%}\")\nprint(f\"Parameter Accuracy: {metrics.parameter_accuracy:.2%}\")\nprint(f\"Execution Success Rate: {metrics.execution_success_rate:.2%}\")\nprint(f\"Response Quality: {metrics.response_quality:.2%}\")\nprint(f\"Overall Score: {metrics.overall_score:.2%}\")\n```\n\n| Metric | Description |\n|--------|-------------|\n| `tool_selection_accuracy` | How often the model selects the correct tool |\n| `parameter_accuracy` | How often tool parameters match expected values |\n| `execution_success_rate` | Rate of valid, executable tool calls |\n| `response_quality` | Quality score for non-tool responses |\n| `overall_score` | Weighted combination of all metrics |\n\n### Accessing Individual Predictions\n\n```python\nresults = evaluator.evaluate(dataset=eval_dataset)\n\n# Iterate through individual sample evaluations\nfor pred in results.predictions:\n    print(f\"Sample {pred.sample_id}:\")\n    print(f\"  Query: {pred.query}\")\n    print(f\"  Expected Tool: {pred.expected_tool}\")\n    print(f\"  Predicted Tool: {pred.predicted_tool}\")\n    print(f\"  Tool Correct: {pred.tool_selection_correct}\")\n    print(f\"  Params Correct: {pred.parameters_correct}\")\n    if pred.error:\n        print(f\"  Error: {pred.error}\")\n```\n\n### Evaluation from JSONL File\n\n```python\nfrom deepfabric.evaluation import Evaluator, EvaluatorConfig, InferenceConfig\n\nconfig = EvaluatorConfig(\n    dataset_path=\"eval_dataset.jsonl\",  # Load from file instead\n    inference_config=InferenceConfig(\n        model_path=\"./my-model\",\n        backend=\"transformers\",\n    ),\n    output_path=\"results.json\",\n)\n\nevaluator = Evaluator(config)\nresults = evaluator.evaluate()  # No dataset argument needed\n```\n\n### Using Ollama Backend\n\n```python\nfrom deepfabric.evaluation import Evaluator, EvaluatorConfig, InferenceConfig\n\nconfig = EvaluatorConfig(\n    inference_config=InferenceConfig(\n        model_path=\"llama3.2:latest\",  # Ollama model name\n        backend=\"ollama\",\n        temperature=0.1,\n    ),\n)\n\nevaluator = Evaluator(config)\nresults = evaluator.evaluate(dataset=eval_dataset)\n```\n\n## Providers\n\n| Provider | Local/Cloud | Best For |\n|----------|-------------|----------|\n| OpenAI | Cloud | High quality, complex tasks |\n| Anthropic | Cloud | Nuanced reasoning |\n| Google Gemini | Cloud | Cost-effective at scale |\n| Ollama | Local | Privacy, unlimited generation |\n| OpenRouter | Cloud | Flexible model choice |\n\n## Tool Tracing with Spin\n\nDeepFabric supports **real tool execution** during dataset generation using the [Spin Framework](https://www.fermyon.com/spin). Instead of simulating tool outputs, tools actually execute in isolated WebAssembly sandboxes, producing authentic training data.\n\n### Why Real Execution Matters\n\nTraditional synthetic data generators simulate tool outputs, which creates unrealistic training data:\n\n```\n# Simulated (problematic)\nAgent: read_file(\"config.json\")\nResult: {\"setting\": \"value\"}  # LLM hallucinated this content\n```\n\nWith Spin integration, tools execute against real state:\n\n```\n# Real execution (accurate)\nAgent: read_file(\"config.json\")\nResult: FileNotFound  # Actual filesystem state\nAgent: write_file(\"config.json\", \"{...}\")\nResult: Written 42 bytes  # Real operation\n```\n\n### ReAct-Style Execution\n\nDeepFabric uses a ReAct (Reason-Act-Observe) loop for tool calling. The agent observes real results before deciding the next action:\n\n```\nStep 1: Agent thinks \"I should check if config exists\"\n        -\u003e Calls read_file(\"config.json\")\n        -\u003e Observes: FileNotFound\n\nStep 2: Agent thinks \"Config doesn't exist, I'll create it\"\n        -\u003e Calls write_file(\"config.json\", content)\n        -\u003e Observes: Success\n```\n\nThis produces training data where decisions are based on actual observations, not hallucinated assumptions.\n\n### Configuration\n\nEnable tool tracing in your YAML config:\n\n```yaml\ngeneration:\n  conversation:\n    type: cot\n    reasoning_style: agent\n\n  tools:\n    spin_endpoint: \"http://localhost:3000\"  # Spin service URL\n    available:                              # Filter to specific tools\n      - read_file\n      - write_file\n      - list_files\n    max_agent_steps: 5                      # Max ReAct iterations\n\n    # Optional: Seed initial state for scenarios\n    scenario_seed:\n      files:\n        \"config.json\": '{\"debug\": true}'\n```\n\n### Built-in VFS Tools\n\nDeepFabric includes a virtual filesystem (VFS) component with these tools:\n\n| Tool | Description |\n|------|-------------|\n| `read_file` | Read content from a file |\n| `write_file` | Write content to a file |\n| `list_files` | List all files in the session |\n| `delete_file` | Delete a file |\n\nEach session gets an isolated filesystem - changes don't persist between samples.\n\n### Running Spin Locally\n\n```bash\ncd tools-sdk\nspin build\nspin up\n```\n\nThe Spin service runs at `http://localhost:3000` by default.\n\n### Adding Custom Tools\n\nYou can extend DeepFabric with custom tools written in Python, JavaScript, Go, or Rust. See [tool-traces.md](./tool-traces.md) for detailed documentation on:\n\n- Creating custom Spin components\n- Tool definition schemas\n- Multi-language examples\n- Containerization and deployment\n\n## Resources\n\n- [Documentation](https://always-further.github.io/deepfabric/)\n- [Examples](./examples/README.md)\n- [Tool Tracing Guide](./tool-traces.md)\n- [Discord](https://discord.gg/pPcjYzGvbS)\n- [Issues](https://github.com/always-further/deepfabric/issues)\n\n## Development\n\n```bash\ngit clone https://github.com/always-further/deepfabric\ncd deepfabric\nuv sync --all-extras\nmake test\n```\n\n## Analytics\n\nWe collect anonymous usage metrics to improve DeepFabric. No personal data, prompts, or API keys are collected.\n\n```bash\n# Disable analytics\nexport ANONYMIZED_TELEMETRY=False\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falways-further%2Fdeepfabric","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Falways-further%2Fdeepfabric","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falways-further%2Fdeepfabric/lists"}