⚠️ WARNING: BROKEN MODEL ⚠️
Model Checkpoint Advisory
🔴 STATUS: BROKEN
🚫 NOT FOR PRODUCTION
🔬 RESEARCH USE ONLY
This model checkpoint is currently broken and is not production-ready. It has not passed the validation, stability, or safety checks required for deployment, and must not be used to serve live traffic or power user-facing features.
⚠️ Known Issues
- Hallucinations — may generate plausible-sounding but false or fabricated information
- Repetitive outputs — may loop, stall, or repeat phrases and content
- Incorrect information — factual accuracy is not reliable
- Unreliable instruction following — may ignore, misread, or partially follow prompts
🔬 Recommended Use
Use this checkpoint only for internal testing, research, and debugging. Do not integrate it into production systems, customer-facing applications, or any environment where correctness or reliability is required.
This notice must remain visible until the checkpoint is fixed, replaced, or formally deprecated.
Nero-800M
A lightweight instruction-tuned language model optimized for efficient local AI.
Nero-800M
Nero-800M is a compact open-source language model based on Qwen3.5-0.8B, fine-tuned to improve instruction following, conversational ability, and reasoning behavior while maintaining fast and efficient inference.
Nero-800M is designed for users who want a capable AI assistant that can run locally on limited hardware.
Model Details
| Detail | Value |
|---|
| Base Model | unsloth/Qwen3.5-0.8B |
| Parameters | ~800M |
| Fine-tuning | QLoRA + LoRA SFT |
| Context Length | 2048 tokens |
| Architecture | Decoder-only Transformer |
Training
Nero-800M was trained using parameter-efficient fine-tuning methods:
- Supervised Fine-Tuning (SFT)
- QLoRA
- LoRA refinement
Training focused on:
- Instruction following
- Helpful assistant behavior
- Response formatting
- Reasoning improvements
Intended Use
Nero-800M is suitable for:
- Local AI assistants
- Edge devices
- Lightweight chat applications
- AI experimentation
- Educational projects
Limitations
Due to its compact size, Nero-800M may:
- Have weaker reasoning than larger models
- Make factual mistakes
- Struggle with complex multi-step tasks
For more demanding workloads, larger Nero models are recommended.
Credits
Built with:
- Qwen3.5
- Unsloth
- Open-source AI community
