hsh-finetune-dataset
shallowio.github.hshintelligence/data-on-demand · Verify this server
Made-to-order, answer-verified datasets for LLM fine-tuning. Describe the task (e.g. 'step-by-step math reasoning', 'SQL generation', 'instruction-following for support replies') and we deliver a clean, HuggingFace-ready dataset in Alpaca schema (instruction/input/output), deduplicated, train/val/test split, with every checkable answer verified in code. Drop the repo straight into Gradients (SN56), TRL, Axolotl, or Unsloth. Verified sample live: huggingface.co/datasets/HSH-Intelligence/verified-math-reasoning-3k. Tier S: 1-2K rows ($75). Tier M: 2-5K rows ($150). Tier L: 5-10K rows ($300). Custom/larger scoped on request.
1 trials · measured 50 days ago
hsh-finetune-dataset scores 100.0/100 on Vouch's measured behaviour index, from 1 real invocation trials against io.github.hshintelligence/data-on-demand, measured 31 Aug 2026 under methodology v0.2.0. Every measured component scored 100.
Component breakdown
| Component | Weight | Value |
|---|---|---|
| Reliability | 35% | not applicable |
| Schema integrity | 25% | 100.0 |
| Failure behaviour | 15% | not applicable |
| Latency | 15% | not applicable |
| Concurrency | 10% | not applicable |
Tool details
- Transport
- remote
- Credential class
- self-provisionable
- Category
- Communication
- Input schema
- not declared
- Output schema
- not declared
- Side-effect classification
- unclassified
Score history
| Day | Score | Tier | Methodology |
|---|---|---|---|
| 2026-08-31 | 100.0 | shallow | v0.2.0 |
Probe evidence
| Probe | Outcomes |
|---|---|
| schema_integrity | pass: 1 |
Raw request/response logs are not archived yet — the outcome counts above are drawn directly from every recorded trial.
Embed this score
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[](https://vouch.tools/tools/46019333-5081-4735-b3a8-12f0d0369682)