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/fine-tuningnpx skills add ProfSynapse/Synaptic-Tuner --skill fine-tuninggit clone --depth 1 https://github.com/ProfSynapse/Synaptic-TunerWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/profsynapse/synaptic-tuner/fine-tuning)<a href="https://agentmods.dev/skills/profsynapse/synaptic-tuner/fine-tuning"><img src="https://agentmods.dev/badge/skills/profsynapse/synaptic-tuner/fine-tuning.svg" alt="Measured on agentmods" height="20"></a>What 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 | $0.00113 | $0.09234 |
| Opus 5 | $0.00056 | $0.04617 |
| Sonnet 5 | $0.00023 | $0.01847 |
| Haiku 4.5 | $0.00011 | $0.00923 |
Grade A, and why
fine-tuning 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 4d 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 — 582 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fine-Tuning Pipeline
Train language models with SFT, KTO, and GRPO locally or on supported cloud providers. This skill also covers the Karpathy-style experiment loop, checkpoint evaluation, LoRA surgery, and the current HF Jobs operational path.
Quick Reference
| Task | Command |
|---|---|
| Interactive menu | ./run.sh → Train |
| Local Docker config run | python tuner.py local-run --job-config Trainers/recipes/<recipe>.yaml --yes |
| SFT training | cd Trainers/sft && python train_sft.py --model-size 7b |
| KTO training | cd Trainers/kto && python train_kto.py --model-size 7b |
| GRPO training | cd Trainers/grpo && python train_grpo.py |
| Pivot-profile GRPO dataset | cd Trainers/grpo && python train_grpo.py --config configs/pivot_config.yaml --pivot-profile-only |
| GRPO with pivot filtering | cd Trainers/grpo && python train_grpo.py --config configs/pivot_config.yaml |
| Env-backed GRPO | cd Trainers/grpo && python train_env_grpo.py --config ./configs/env_config.yaml --dry-run |
| Experiment loop | python tuner.py experiment-loop --experiment-config configs/flywheel/experiment_loop.yaml |
| LoRA surgery | python tuner.py surgery --surgery-config configs/lora_surgery.yaml |
| HF custom job | python tuner.py cloud-run --job-config Trainers/recipes/<recipe>.yaml |
| Canonical HF train+eval | python tuner.py cloud-pipeline --method sft --preset full |
| Full experiment bundle | python tuner.py run-experiment --experiment-spec Trainers/cloud/experiments/<spec>.yaml --yes |
| Evolutionary SFT smoke test | python tuner.py run-experiment --experiment-spec Trainers/cloud/experiments/<evolutionary-spec>.yaml --yes |
| Staggered experiment batch | python3 scripts/launch_experiment_batch.py Trainers/cloud/experiments/<spec1>.yaml Trainers/cloud/experiments/<spec2>.yaml --yes |
| One-shot RunPod wrapper job | python3 scripts/runpod_run_job.py --run-tag <tag> --repo-url <git-url> --commit <full-sha> --wrapper <repo-relative.sh> --dry-run |
| Detached Modal job (survives client exit) | modal run --detach <app_module>::<function> |
| Blind hardware plan | python tuner.py plan-hardware --experiment-spec Trainers/cloud/experiments/<spec>.yaml |
| Analyze finished experiment | python tuner.py analyze-experiment --experiment-id latest |
| Analyze/prune dataset from loss | python3 scripts/prune_dataset_from_loss.py --dataset-path ... --experiment-id ... --analyze-only |
| Standalone prompt optimization | python tuner.py prompt-optimize --prompt-opt-config configs/prompt_optimization/NAME.yaml |
| Prompt-optimize SynthChat generation | python -m SynthChat.run generate --prompt-opt-config configs/prompt_optimization/NAME.yaml [options] |
| Analyze bucket-backed run | python tuner.py bucket analyze --path runs/hf_jobs/sft/<run-prefix>/ |
| Read bucket artifact | python tuner.py bucket read --path runs/.../logs/training_latest.jsonl --jsonl-latest --pretty |
| List bucket prefix | python tuner.py bucket list --path runs/hf_jobs/sft/<run-prefix>/ --limit 20 |
| Pull bucket prefix locally | python tuner.py bucket pull --path runs/hf_jobs/sft/<run-prefix>/ --dest . |
| Push local artifact to bucket | python tuner.py bucket push --path local/results.json --dest runs/manual_uploads/ |
| Live HF job list | python tuner.py cloud-jobs list |
| Live HF job logs | python tuner.py cloud-jobs logs --job professorsynapse/<job-id> --tail 200 |
| Cloud eval against a run | python tuner.py cloud-eval --run latest --preset full |
| Local deterministic vLLM generation | VLLM_BATCH_INVARIANT=1 python tuner.py batch-generate --engine vllm ... |
| HF gym against trained model | python tuner.py cloud-gym --run latest --method sft |
| Warm Space scaffold | python3 Trainers/cloud/scripts/manage_space.py render --template vllm_warm --output-dir /tmp/my-space --base-image ghcr.io/<org>/<image>:<tag> |
| Warm Space deploy | python3 Trainers/cloud/scripts/manage_space.py deploy --space-id <user>/<space> --template vllm_warm --base-image ghcr.io/<org>/<image>:<tag> --hardware a10g-small --sleep-time 3600 --var BASE_MODEL=<model> |
| ML training | python tuner.py ml train --config Trainers/ml/configs/templates/regression.yaml |
What ships with it
35 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.
- configs/dora.yaml 971 B
- configs/eva.yaml 1.1 KB
- configs/experiment_model_gpu_template.yaml 1.9 KB
- configs/gemma4_e4b_a100.yaml 1.3 KB
- configs/grpo_minimal.yaml 813 B
- configs/loftq.yaml 777 B
- configs/olora.yaml 851 B
- configs/pissa.yaml 932 B
- configs/qlora_dora.yaml 802 B
- configs/qwen3_4b_a100.yaml 1.0 KB
- configs/qwen35_4b_a100.yaml 1.3 KB
- configs/regret_free.yaml 1.3 KB
- protocols/environment-backed-alignment-pipeline.md 10 KB
- reference/checkpoint-evaluation.md 2.2 KB
- reference/cloud-experiment-launching.md 6.8 KB
- reference/cloud-training.md 15 KB
- reference/dataset-formats.md 5.6 KB
- reference/experiment-loop.md 4.0 KB
- reference/grpo-training.md 15 KB
- reference/hf-spaces-warm-iteration.md 2.6 KB
- reference/kto-training.md 5.2 KB
- reference/lora-surgery.md 4.8 KB
- reference/lora-techniques.md 12 KB
- reference/modal-jobs.md 5.2 KB
- reference/model-presets.md 5.1 KB
- reference/runpod-jobs.md 13 KB
- reference/sft-training.md 12 KB
- reference/training-config.md 14 KB
- reference/troubleshooting.md 5.4 KB
- scripts/hf_jobs_hardware.py 4.9 KB runs code
- scripts/launch_experiment_batch.py 4.0 KB runs code
- scripts/prune_dataset_from_loss.py 25 KB runs code
- scripts/read_bucket_artifact.py 1.1 KB runs code
- scripts/runpod_run_job.py 16 KB runs code
- scripts/split_for_gspo.py 5.9 KB runs code
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.
- 4d ago First seen · 582 lines · 113 tokens per session scan A 77fb4d667898
fine-tuning is a skill published in the GitHub repository ProfSynapse/Synaptic-Tuner (27 stars, last pushed today), licensed MIT. It adds 113 tokens to every session and 9,234 once invoked, about $0.0006 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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