Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/profsynapse/synaptic-tuner/upload-deploymentnpx skills add ProfSynapse/Synaptic-Tuner --skill upload-deploymentgit clone --depth 1 https://github.com/ProfSynapse/Synaptic-TunerWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00090 | $0.01964 |
| Opus 5 | $0.00045 | $0.00982 |
| Sonnet 5 | $0.00018 | $0.00393 |
| Haiku 4.5 | $0.00009 | $0.00196 |
Grade A, and why
upload-deployment scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Upload & Deployment
Upload trained models to HuggingFace with optional GGUF conversion and model card generation.
For cloud training, provider-native storage remains the source of truth. Hugging Face Hub publishing is optional and only applies to final_model.
Quick Reference
| Task | Command |
|---|---|
| Interactive menu | ./run.sh → Upload |
| Upload merged 16-bit | python3 .skills/upload-deployment/scripts/upload_model.py MODEL_PATH user/repo --save-method merged_16bit |
| Upload with GGUF | python3 .skills/upload-deployment/scripts/upload_model.py MODEL_PATH user/repo --save-method merged_16bit --create-gguf |
| Upload LoRA only | python3 .skills/upload-deployment/scripts/upload_model.py MODEL_PATH user/repo --save-method lora |
| Merge LoRA manually | ./run.sh → Merge LoRA |
| Convert to GGUF only | ./run.sh → Convert |
| Cloud GGUF conversion | python tuner.py cloud-run --job-config Trainers/recipes/gguf_conversion.yaml --yes |
| Full pipeline | ./run.sh → Full Pipeline (Train → Upload → Eval) |
Save Strategies
| Strategy | Size (7B) | GPU Required | Best For |
|---|---|---|---|
lora |
~100-500 MB | No | Sharing adapters, fast upload |
merged_16bit |
~14 GB | Yes | Production inference, GGUF source |
merged_4bit |
~4 GB | Yes | Smaller footprint, slight quality loss |
GGUF Quantizations
| Format | Size (7B) | Quality | Use Case |
|---|---|---|---|
| Q8_0 | ~7 GB | Highest | Best quality, more RAM |
| Q5_K_M | ~5 GB | High | Good balance |
| Q4_K_M | ~4 GB | Good | Most popular, efficient |
Key Directories
.skills/upload-deployment/scripts/upload_model.py— Generic upload entry pointscripts/cloud_gguf_convert.py— Cloud GGUF conversion CLI (download → convert → upload)Trainers/recipes/gguf_conversion.yaml— HF Jobs recipe (target: cloud) for cloud GGUF conversionshared/upload/— Upload orchestrator and strategiesshared/upload/converters/— GGUF and WebGPU convertersshared/model_loading/— Model loading and LoRA merge utilities
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 156 lines · 90 tokens per session scan A 1800749ecd99
upload-deployment is a skill published in the GitHub repository ProfSynapse/Synaptic-Tuner (27 stars, last pushed 2d ago), licensed MIT. It adds 90 tokens to every session and 1,964 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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