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LFM2-350M-lmsguide-GGUF
MadlabOSS/LFM2-350M-lmsguide-GGUF
LFM2-350M-lmsguide-GGUF is a machine learning model from MadlabOSS. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as other.
The LMS Guide 350m 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 fo…
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At a glance
- License
- other
- Access
- Public
- Created
- Dec 25, 2025
- Updated
- Jan 7, 2026
- SHA
- 8af12d51
Base models
Model card
From the Hugging Face model README
LMS Guide 350m
🧠 Overview
The LMS Guide 350m 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: LFM2‑350m
Parameter Count: 350 Million
Training Type: Supervised fine‑tuning
Sequence Length: 2048
Precision: FP16
Framework: PyTorch / Transformers
📦 Training Data
The model was trained on:
- ~36,000 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
🏋️ Training Procedure
Hyperparameters
- Epochs: 6
- Batch size: 64
- Learning rate: cosine schedule, peak ~4e‑5
- Optimizer: AdamW
- Gradient clipping: 1.0
- Gradient accumulation: 1
Hardware
Training was performed on:
- RTX Pro 6000 Blackwell (96GB)
- Dual RTX 3090 (Magic Judge)
📊 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
- License
- other
- Created
- Dec 25, 2025
- Updated
- Jan 7, 2026