| name | unsloth-jobs-training |
| description | Submit a LoRA fine-tuning job to Hugging Face Jobs using Unsloth optimizations on LFM2.5-1.2B-Instruct. Use when the user wants to train or fine-tune a model on HF Jobs cloud GPUs. |
Submit a LoRA fine-tuning job to Hugging Face Jobs using Unsloth.
Arguments
$ARGUMENTS should contain the dataset ID and optionally the output repo ID.
If not provided, ask the user for: dataset ID, output repo ID, and run name.
Default Configuration (proven from LFM2.5 mobile-actions training)
- Base model:
LiquidAI/LFM2.5-1.2B-Instruct
- LoRA rank/alpha: 16
- Batch size: 2 (effective 8 with grad accumulation 4)
- Learning rate: 2e-4, linear decay
- Epochs: 3
- Max seq length: 2048
- Eval split: 2% from training data
- Hardware:
a10g-large (24GB VRAM)
- Timeout: 4h
- Trackio space:
kshitijthakkar/trackio
Step 1: Confirm authentication
hf whoami
Step 2: Submit the job
Run this command (substitute <DATASET_ID>, <OUTPUT_REPO>, <RUN_NAME>):
hf jobs uv run \
--flavor a10g-large \
--timeout 4h \
--secrets HF_TOKEN \
"C:\Users\kshit\.claude\plugins\cache\huggingface-skills\hugging-face-model-trainer\3f4f55d6264b\scripts\unsloth_sft_example.py" \
-- \
--dataset <DATASET_ID> \
--num-epochs 3 \
--lora-r 16 \
--lora-alpha 16 \
--batch-size 2 \
--gradient-accumulation 4 \
--learning-rate 2e-4 \
--max-seq-length 2048 \
--eval-split 0.02 \
--trackio-space kshitijthakkar/trackio \
--run-name <RUN_NAME> \
--seed 3407 \
--output-repo <OUTPUT_REPO>
Step 3: Report back to user
After submission, provide:
- Job ID (from command output)
- Job URL:
https://huggingface.co/jobs/kshitijthakkar/<job-id>
- Trackio dashboard:
https://huggingface.co/spaces/kshitijthakkar/trackio
- Estimated completion: ~2-3 hours for 30k samples, 3 epochs on A10G
Check job status
PYTHONUTF8=1 hf jobs inspect <job-id>
CLI syntax rules (critical)
- All HF Jobs flags (
--flavor, --timeout, --secrets) MUST come before the script path
- Use
-- (double dash) to separate HF Jobs flags from script arguments
PYTHONUTF8=1 prefix avoids Windows encoding errors in log output- Local script files are uploaded automatically by the CLI
Dataset requirements
Dataset must have a messages, conversations, or conversation column with lists of {role, content} dicts. The script handles column renaming and ChatML formatting automatically.
Validated example (working job)
hf jobs uv run \
--flavor a10g-large \
--timeout 4h \
--secrets HF_TOKEN \
"C:\Users\kshit\.claude\plugins\cache\huggingface-skills\hugging-face-model-trainer\3f4f55d6264b\scripts\unsloth_sft_example.py" \
-- \
--dataset kshitijthakkar/liquidchat-lora-dataset \
--num-epochs 3 \
--lora-r 16 --lora-alpha 16 \
--batch-size 2 --gradient-accumulation 4 \
--learning-rate 2e-4 --max-seq-length 2048 \
--eval-split 0.02 \
--trackio-space kshitijthakkar/trackio \
--run-name liquidchat-lora-v1 \
--seed 3407 \
--output-repo kshitijthakkar/LFM2.5-1.2B-Instruct-liquidchat-lora
Job ID: 699d9b0f52d1c53b7df7d678 — GPU: A10G — Started: 2026-02-24
Hardware tiers and batch sizes
| Flavor | VRAM | Batch size | Eff. batch | Est. time | Plan required |
|---|
a10g-large | 24GB | 2 | 8 | ~2-3h | Pro |
l40sx1 | 48GB | 4 | 16 | ~1-1.5h | Pro |
a100-large | 80GB | 8 | 32 | ~45-60m | Enterprise |
Gradient accumulation is always 4. Adjust batch size to match VRAM.
Troubleshooting
| Error | Fix |
|---|
402 Payment Required (insufficient credits) | Top up at https://huggingface.co/settings/billing |
402 Payment Required (plan restriction) | GPU tier requires higher plan — use a10g-large as fallback |
UnicodeEncodeError in CLI | Job still submitted fine — use PYTHONUTF8=1 prefix |
| OOM on GPU | Reduce --batch-size 1, increase --gradient-accumulation 8 |
| Job timeout | Increase --timeout 6h or reduce --num-epochs |
| Hub push fails | Verify --secrets HF_TOKEN is set and token has write access |
Next step after training
Run /lora-to-cactus-hub to merge adapters and convert to Cactus format for mobile deployment.
Supporting files
This skill includes reference material and alternative scripts:
