qwen-linux-copilot-fresh-v3 is a LoRA adapter on top of Qwen/Qwen3-4B-Instruct-2507 trained for Linux server troubleshooting and copilot-style assistance. It is the best checkpoint from this training series because it preserved concrete first actions better than later correction passes while staying more grounded than earlier broad runs.
This adapter is intended for:
Linux troubleshooting assistance
Copilot-style next-step guidance
Safe first-action recommendations
Subsystem-scoped diagnosis for SSH, systemd, nginx, package recovery, storage, databases, containers, and permissions
This adapter is not positioned as a fully autonomous server operator.
Base Model
Base model: Qwen/Qwen3-4B-Instruct-2507
Fine-tuning method: LoRA / PEFT
Precision during training: BF16
Training environment: AMD ROCm on WSL2
Why Fresh V3
Fresh V3 was chosen as the publish candidate because it gave the best overall balance of:
concrete first commands
subsystem grounding
lower cross-domain drift
practical operator usefulness
Compared with later patches, it remained more action-oriented and less generic.
Evaluation Snapshot
On a 200-row holdout, Fresh V3 was the strongest Qwen checkpoint in overall quality.
Key heuristic signals:
apt/dpkg on yum/dnf prompts: 2
SELinux-like prompts drifting to NFS: 0
DB prompts drifting to firewall/SSH: 0
timer prompts with irrelevant commands: 1
Observed strengths:
grounded nginx and PostgreSQL triage
good first-step behavior for disk and storage incidents
low subsystem drift relative to other runs
Observed weaknesses:
some timer/systemd cases still need improvement
restart language is still higher than ideal
not yet a fully trustworthy autonomous Linux operator
Intended Use
Recommended:
interactive Linux copilot
incident triage assistant
first-response diagnosis helper
operator assistant with human review
Not recommended:
unattended destructive actions
unsupervised production remediation
broad autonomous infrastructure control without tool policy and approval gates
Safety Notes
Human review is required before disruptive actions.
The model should be used in diagnosis-first mode.
Restart, package cleanup, firewall changes, and permission changes should remain approval-gated.
Use explicit subsystem scoping in prompts to reduce drift.
Training Data Overview
Primary training base:
balanced fresh Linux copilot dataset: 10,000 train / 1,000 eval
balanced across categories and styles
Additional iterative work:
targeted correction datasets
hard prompt packs
real failure mining from interactive prompt packs
The final publish candidate remains Fresh V3 because later correction passes improved some narrow cases but often reduced practical specificity.
Example Prompt
Users report 502 behind nginx after an app deploy. What should the copilot inspect first?
Example Behavior
Good answers should:
start with systemctl status nginx
inspect recent nginx errors
verify backend listener or health endpoint
avoid drifting into unrelated Apache or package-manager paths
Limitations
still imperfect on timer-specific troubleshooting
can still overuse restart-oriented phrasing in some cases
needs a tool policy layer for safe real-world operator use
should be evaluated further on real environment logs and tool outputs
Files
Expected files in this adapter repo:
adapter_model.safetensors
adapter_config.json
tokenizer.json
tokenizer_config.json
chat_template.jinja
README.md
License and Attribution
Follow the base model license and preserve attribution to the original base model and any external datasets used in the training workflow.
Included Documentation
Additional documentation is included in the docs/ folder:
