metadata
license: other
license_name: lfm1.0
license_link: https://huggingface.co/LiquidAI/LFM2-350M/blob/main/LICENSE
datasets:
- Archi-medes/LabGuide_Preview
base_model:
- LiquidAI/LFM2-700M
base_model_relation: finetune
pipeline_tag: question-answering
LabGuide Preview Model
Model Summary
The LabGuide Preview Model is a demonstration release built entirely with Madlab, using its synthetic dataset generator and training workflow.
It is based on LiquidAI/LFM2-700M, adapted to showcase Madlab’s end-to-end capabilities for dataset creation, model training, and assistant deployment.
This model illustrates how applications can leverage Madlab to train their own assistants in a reproducible and accessible way.
It is not intended for production use, but rather as a preview for contributors, collaborators, and community feedback.
Training Data
- Source: Synthetic dataset generated entirely with Madlab’s dataset generator.
- Purpose: Designed to demonstrate Madlab’s ability to produce structured, reproducible training data.
- Scope: Preview-scale dataset, not representative of real-world or production-ready corpora.
Training Process
- Framework: Madlab training pipeline.
- Base Model: LiquidAI/LFM2-700M.
- Workflow: Synthetic dataset generation → Madlab training loop → Magic Judge Evaluation → Preview model release.
- Objective: Demonstrate Madlab’s integrated workflow for building application-specific assistants.
Intended Uses
- Contributor onboarding and workflow validation.
- Demonstration of Madlab’s synthetic dataset generator and training pipeline.
- Benchmarking and experimentation in controlled preview settings.
Limitations
- Demo-only: Not suitable for production or deployment in real-world applications.
- Synthetic data: Training data is fully synthetic and may not reflect natural language distributions.
- Preview scale: Model performance is illustrative, not optimized for accuracy or robustness.
Ethical Considerations
- This model is provided for demonstration and educational purposes.
- It should not be used in applications where accuracy, safety, or reliability are critical.
- Contributors are encouraged to treat outputs as illustrative examples only.
Acknowledgements
- Base model: LiquidAI/LFM2-700M.
- Built and trained with Madlab.