language:

• en
  license: apache-2.0
  tags:
• drug-discovery
• formulation-development
• pharmaceutical
• chemistry
• fine-tuning
• instruction-tuning
• heretic
• uncensored
• chatml
• multi-turn
  pretty_name: RED-PILL
  size_categories:
• 1K<n<10K
  task_categories:
• text-generation
• question-answering
  configs:
• config_name: default
  data_files:
  • split: train
    path: red-pill-full.jsonl
• config_name: gold_tier
  data_files:
  • split: train
    path: red-pill-full_gold_tier.jsonl
• config_name: top_tier
  data_files:
  • split: train
    path: red-pill-full_top_tier.jsonl

🔴 RED-PILL

Research Enhanced Dataset for Pharmaceutical Innovation in Learning & Language

The first open instruction-tuning dataset for drug discovery & formulation development.

Built for fine-tuning Heretic-ablated models that won't refuse your pharmaceutical R&D questions.

---

⚡ Quick Start

from datasets import load_dataset

# Load the full dataset
ds = load_dataset("saidutta69/red-pill-drug-discovery-formulation", data_files="red-pill-full.jsonl")

# Load gold tier (elite 17 instructions)
ds = load_dataset("saidutta69/red-pill-drug-discovery-formulation", data_files="red-pill-full_gold_tier.jsonl")

# Load top tier (145 grade A+B instructions)
ds = load_dataset("saidutta69/red-pill-drug-discovery-formulation", data_files="red-pill-full_top_tier.jsonl")

📊 Dataset at a Glance

🎯 Quality Distribution

Grade A (≥0.75):  ████░░░░░░░░░░░░░░░░  17 (1.6%)
Grade B (≥0.60):  ████████████░░░░░░░░  128 (12.2%)
Grade C (≥0.45):  ████████████████████  789 (75.3%)
Grade D (≥0.30):  ██░░░░░░░░░░░░░░░░░░  102 (9.7%)
Grade F (<0.30):  ░░░░░░░░░░░░░░░░░░░░  11 (1.1%)

🧬 The Six Pillars

📋 Data Format

Each instruction follows the ChatML format with rich metadata:

{
  "messages": [
    {
      "role": "user",
      "content": "I have a BCS Class II compound with solubility 0.5 µg/mL and LogP 4.2..."
    },
    {
      "role": "assistant",
      "content": "## Solubility Enhancement Strategy\n\n### Compound Profile:\n- **Solubility:** 0.5 µg/mL (very poor)\n- **LogP:** 4.2 (lipophilic)\n..."
    }
  ],
  "metadata": {
    "pillar": "formulation",
    "domain": "solubility enhancement",
    "skill_type": "requires-reasoning",
    "source": "formulation_knowledge_base",
    "difficulty": "expert"
  }
}

📊 Metadata Fields

🎯 Quality Scoring System

Every instruction is scored across 8 dimensions:

🏆 Grade Scale

🔬 Evaluation Benchmarks

The dataset includes 32 evaluation questions across 5 categories:

Run Evaluation

# Download benchmark
python -c "
from datasets import load_dataset
ds = load_dataset('saidutta69/red-pill-drug-discovery-formulation', data_files='benchmarks/benchmark_suite.json')
ds['train'].to_json('benchmark_suite.json')
"

# Run evaluation (after generating model responses)
python benchmarks/evaluate.py \
    --responses your_model_responses.jsonl \
    --benchmark benchmark_suite.json

🚀 Recommended Fine-Tuning Setup

Base Model

Any Heretic-ablated model works:

Training Configuration

from transformers import TrainingArguments
from peft import LoraConfig

# LoRA config (recommended for Heretic models)
lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    target_modules=["q_proj", "v_proj", "k_proj", "o_proj"],
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

# Training args
training_args = TrainingArguments(
    output_dir="./red-pill-heretic",
    num_train_epochs=3,
    per_device_train_batch_size=4,
    gradient_accumulation_steps=8,
    learning_rate=2e-5,
    weight_decay=0.01,
    warmup_ratio=0.1,
    lr_scheduler_type="cosine",
    fp16=True,
    logging_steps=10,
    save_strategy="epoch",
)

⏱️ Expected Training Time

📈 Data Sources & Provenance

⚠️ Disclaimer

This dataset is for research purposes only. It should not be used as the sole basis for pharmaceutical decisions. Always consult qualified professionals for drug development activities.

The knowledge contained in this dataset reflects publicly available information and expert opinions. It may not represent the current state of the art in all areas.

📚 Citation

@dataset{red_pill_2025,
  title={RED-PILL: Research Enhanced Dataset for Pharmaceutical Innovation in Learning and Language},
  author={Saidutta Abhishek Dash},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/saidutta69/red-pill-drug-discovery-formulation},
  description={Instruction-tuning dataset for drug discovery and formulation development.}
}

📄 License

Apache License 2.0

Built with 🔴 by Saidutta

Fine-tune models that won't refuse your pharmaceutical R&D questions.