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causabr-impactcopy-adapter
Fernandosr85/causabr-impactcopy-adapter
causabr-impactcopy-adapter is a text generation model from Fernandosr85. Use it when you need the model to write or continue text. It is set up for peft. The card lists the license as apache-2.0.
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Public
Updated Jul 4, 2026
Repo size
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At a glance
- Task
- Text Generation
- Library
- peft
- License
- apache-2.0
- Access
- Public
- Created
- Jun 22, 2026
- Updated
- Jul 4, 2026
- SHA
- 2eaa1a43
Base models
Model card
From the Hugging Face model README
CausaBR-ImpactCopy — Brazilian Nonprofit Copywriting Adapter
LoRA adapter for ethical copywriting for the Brazilian third sector (ONGs, OSCIPs, institutes, and social foundations), fine-tuned on Mixtral-8x7B-Instruct-v0.1 via Adaption's AutoScientist platform.
Covers 12 distinct communication formats across fundraising, ESG proposals, impact reports, campaigns, social media, grant applications, beneficiary communication, volunteer mobilization, transparency notes, advocacy, and crisis communication — all in PT-BR.
Evaluation results
Training Winrates
The adapted model outperforms the base model with 69% win rate vs 31% on held-out copywriting evaluation — a +122% relative improvement over the base model.
| Model | Win Rate |
|---|---|
Base (Mixtral-8x7B-Instruct-v0.1) | 31% |
Adapted (causabr-impactcopy) | 69% |
Train/Eval Metrics
| Metric | Value |
|---|---|
| Initial train loss | ~2.00 |
| Final validation loss | ~0.93 |
| Loss reduction | ~54% |
| Peak learning rate | 1.0e-6 |
| Training steps | 204 |
| LR scheduler | cosine (warmup) |
| Gradient norm | spike → stable |
Train loss (cyan) converged steadily over 204 steps. Validation loss (black dots) tracked closely, confirming generalization without overfitting. Learning rate followed cosine schedule with warmup, peaking around step 30 then decaying to near-zero. Gradient norm stabilized after initial spike, indicating stable optimization throughout.
Dataset quality
| Metric | Value |
|---|---|
| Dataset grade | A |
| Score before / after | 8.0 → 9.4 |
| Quality improvement | +17.5% (Adaption Adaptive Data remastering) |
| Total examples | 1,200 instruction/completion pairs |
Model details
| Field | Value |
|---|---|
| Base model | mistralai/Mixtral-8x7B-Instruct-v0.1 (46.7B) |
| Trained model name | adaption_mixtral_8x7b_instruc_ongbr_impact_copy_2612056c |
| Training method | Supervised Fine-Tuning (SFT) + LoRA |
| Epochs | 3 |
| Data format | Chat (instruction/completion) |
| Domain | Brazilian nonprofit copywriting — PT-BR |
| Category | Marketing |
Training dataset
Fernandosr85/adaption-ongbr-impact-copy
1,200 instruction/completion pairs across 12 copy types:
| Type | Format | Channel | Examples |
|---|---|---|---|
| A | Fundraising appeal — individual donors | Email / landing page | 100 |
| B | Corporate partnership proposal — ESG | Proposta / deck | 100 |
| C | Impact report narrative | Relatório anual / PDF | 100 |
| D | Seasonal campaign | Email / redes sociais | 100 |
| E | Instagram post | Instagram feed | 100 |
| F | LinkedIn post | 100 | |
| G | Grant application | Formulário de edital | 100 |
| H | Beneficiary communication | WhatsApp / SMS | 100 |
| I | Volunteer mobilization | Email / redes sociais | 100 |
| J | Transparency note | Newsletter / site | 100 |
| K | Advocacy and incidence | Email / petição | 100 |
| L | Crisis communication | Site / assessoria | 100 |
Quality controls
8-dimension rubric (16 pts max): clarity of purpose, credibility, proportional emotional appeal, CTA, language fit, tone of voice, beneficiary dignity, differentiation. Dataset mean: 11.74/16.
3-tier dignity gate: blocks poverty porn, fabricated impact data,
and impossible promises. All 1,200 examples passed with
dignity_flag: ok.
Dignity Principle
The core ethical constraint of this adapter: beneficiary language must center agency and resilience, never victimization.
| Example | |
|---|---|
| ✅ Correct | "Marcus quer ser professor." |
| ❌ Blocked | "Marcus, abandonado, precisa da sua ajuda para sobreviver." |
Model repositories
| Platform | Link |
|---|---|
| HuggingFace Model | Fernandosr85/causabr-impactcopy-adapter |
| HuggingFace Dataset | Fernandosr85/adaption-ongbr-impact-copy |
| Kaggle Dataset | fernandosr85/causabr-impactcopy |
Related adapters (same portfolio)
| Adapter | Domain | Category | Win rate |
|---|---|---|---|
| RegTech BR | Brazilian crypto regulatory compliance | Legal | 63% |
| AfroBR-LangBench | Afro-Brazilian Portuguese sociolinguistics | Language | — |
| VozBR-BrandVoice | Citizen complaint → institutional response | Marketing | 79% |
| HealthBR-CoverageGuide | Health plan and SUS navigation | Healthcare | 64% |
| FinRisk-BR | Crypto investor risk assessment | Finance | — |
| CausaBR-ImpactCopy | NGO copywriting PT-BR | Marketing | 69% |
Credits
- Fine-tuning platform: Adaption — AutoScientist & Adaptive Data
- Challenge: AutoScientist Challenge 2026
- Training infrastructure: Adaption compute credits
- Dataset remastering: Adaption Adaptive Data pipeline (Grade A, +17.5% improvement)
- Author: Fernando Rodrigues · Kaggle: fernandosr85 · HuggingFace: Fernandosr85
Disclaimer
Experimental research artifact submitted to the AutoScientist Challenge 2026 (Marketing category). Generated copy requires human review before publication. The dignity gate enforces ethical communication standards but does not substitute for editorial judgment by communications professionals.
- Task
- Text Generation
- Library
- peft
- License
- apache-2.0
- Languages
- pt, en
- Created
- Jun 22, 2026
- Updated
- Jul 4, 2026