LMS Guide 1.2b
🧠 Overview
The LMS Guide 1.2b is part of the MadlabOSS LM Studio Guide family — a lineup of small, efficient, and highly aligned assistant models trained specifically to provide deterministic, hallucination‑resistant guidance for LM Studio users.
This model is trained on a curated dataset of LM Studio–specific instructions, workflows, troubleshooting steps, and conceptual explanations.
🚀 Intended Use
This model is optimized for:
- LM Studio onboarding
- workflow explanations
- feature descriptions
- troubleshooting guidance
- plugin/server integration help
- safe, deterministic assistant behavior
It is not intended as a general‑purpose chatbot.
🧩 Model Details
Base Model: LFM2‑1.2B
Parameter Count: 1.2 Billion
Training Type: Supervised fine‑tuning
Sequence Length: 1024
Precision: FP16
Framework: PyTorch / Transformers
📦 Training Data
The model was trained on:
- 6,000+ LM Studio–specific instruction/response pairs
- Clean, domain‑specific, ontology‑consistent data
- Minor general‑purpose conversational data
- No web‑scraped content
- Full LM Studio Documentation
A 36k+ expanded dataset is planned for v2.0.
🏋️ Training Procedure
Hyperparameters
- Epochs: 6
- Batch size: 16
- Learning rate: cosine schedule, peak ~4e‑5
- Optimizer: AdamW
- Gradient clipping: 1.0
- Gradient accumulation: 1
Hardware
Training was performed on:
- RTX 6000 Ada (96GB) (1.2b + 2.6b)
- Dual RTX 3090 (Magic Judge)
- RTX 3070 (for 0.35B + 0.7b)
📊 Evaluation
Judge Score
Semantic correctness, ontology adherence, and hallucination resistance.
Qualitative Behavior
- Strong adherence to LM Studio terminology
- Low hallucination rate
- Deterministic, predictable responses
- Not optimized for open‑domain reasoning
🔒 Safety
This model is trained exclusively on LM Studio–specific content.
It avoids hallucinating non‑existent LM Studio features and adheres to a strict ontology.
It is not designed for:
- political content
- medical advice
- legal advice
- general‑purpose conversation
⚠️ Limitations
- Not a general assistant
- Not trained for coding, math, or open‑domain reasoning
- May refuse tasks outside LM Studio scope
- Static accuracy metrics underestimate real performance
