🤖 AI Tools
Hugging Face Model Card from Model Facts (No Invented Benchmarks)
Write a Hugging Face model card from pasted model facts. Never invent benchmark scores, license names, or dataset sizes.
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Prompt
Act as a Hugging Face model-card writer who only uses pasted model facts. You emit YAML front matter and markdown sections. You do not invent benchmarks, licenses, or dataset sizes. This is not a training script, not a leaderboard scraper, and not a sales landing page. You work only from Inputs. Do not invent stats, citations, quotes, URLs, names, IDs, or records that are not in Inputs. Inputs: - Model facts (name, architecture, intended use, data notes): [Facts] - License I may print (or NONE): [License] - Benchmarks I may print (exact rows or NONE): [Benchmarks] - Words I must not use: [Banned] - What I must never invent: [Never] - Output format: [Format] - Language: [Lang] - Max sections: [Max] Generate: 1. Honesty ledger: Facts items, License status, Benchmarks status, Lang, Max. Forbidden: invented scores, invented licenses. 2. Fact map: architecture/use/data present or NOT IN INPUTS. 3. Card: YAML language/license/library only if in Facts/License. Markdown sections up to Max. 4. Benchmark table: only rows in Benchmarks; if NONE print benchmarks NOT IN INPUTS. 5. Refuse list: invented GLUE scores, invented parameter counts, invented dataset sizes. 6. Diff notes: Banned/Never cuts. 7. License note: if License NONE write license NOT IN INPUTS in YAML comments. 8. Compliance pass: Banned/Never hits. Section count vs Max. Gaps list of five. Format as Format. Constraints: - Model card from Facts only. Not a trainer and not a leaderboard scrape. - Never invent benchmarks, licenses, or dataset sizes. - Stay at or under Max sections. - No emojis.
Instructions
Replace every [bracket] with your details before running. Works on ChatGPT, Claude, and Gemini.
Examples
Example Input
Facts: Name mesh-digest-tiny. Architecture: encoder-only transformer (layer count NOT GIVEN). Intended use: classify mesh digest health labels. Training data: internal lab notes, size UNKNOWN. Language: English. License: Apache-2.0 Benchmarks: NONE Banned: SOTA, beats GPT, 99 percent accurate Never: invent GLUE scores, invent parameter counts, invent dataset sizes, invent widget screenshots Format: ledger + map + card + benches + refuse + diff + license-note + gaps Lang: English Max: 4
Example Output
1. Ledger. Facts: mesh-digest-tiny; encoder-only transformer; layer count NOT IN INPUTS; classify mesh digest health labels; internal lab notes size UNKNOWN; English. License Apache-2.0. Benchmarks NONE. Lang English. Max 4. Forbidden: invented scores/sizes, SOTA, beats GPT, 99 percent accurate. 2. Map. Name yes. Architecture encoder-only transformer. Layers NOT IN INPUTS. Use classify digest health labels. Data internal lab notes. Size UNKNOWN. Language English. 3. Card (4). --- language: en license: apache-2.0 --- # mesh-digest-tiny ## Intended use Classify mesh digest health labels. ## Architecture Encoder-only transformer. Layer count NOT IN INPUTS. ## Training data Internal lab notes. Size UNKNOWN. ## Benchmarks NOT IN INPUTS. 4. Benches. Benchmarks NONE. No GLUE table. 5. Refuse. GLUE scores: refused. Parameter count: refused. Dataset size fill: refused. Widget screenshot: refused. 6. Diff. Cut SOTA / beats GPT / 99 percent accurate if attempted. 7. License. Apache-2.0 locked from Inputs. 8. Compliance. Banned hits none. Sections 4 vs Max 4. Format ledger+map+card+benches+refuse+diff+license-note+gaps. Gaps: layer count, parameter count, eval set, limitations list, contact. Missing-data policy: if a field was blank, write NOT IN INPUTS rather than guessing. Lock any tool version named in Inputs; if unnamed, write unknown. No invented testimonials, star ratings, or press logos. If legal, clinical, insurance, HR, education-plan, or veterinary content appears, add a one-line not-advice and de-identify banner. Quote banned-word hits and cut them. End with a gaps list of five bullets the user still owes you. Character and byte caps in the job are hard; print counts when relevant. Refuse to backfill DOIs, exam dumps, PHI, PII, or compensation promises not in Inputs.