💊 Red Pill V1

Pharmaceutical AI That Won't Say No

Fine-tuned for drug discovery & formulation development

What is Red Pill?

Red Pill is a 1B-parameter language model fine-tuned on the RED-PILL dataset — 1,047 multi-turn pharmaceutical instructions covering drug discovery, formulation development, synthesis, regulatory affairs, and scientific literature.

Built on the Heretic-abliterated MiniCPM5-1B base, Red Pill won't refuse legitimate pharmaceutical R&D questions — from molecular design to formulation strategy to regulatory pathways.

"In the pharmaceutical world, the model that says 'I can't help with that' is the model that gets replaced."

🧪 Capabilities

📊 Evaluation

Tested on 32 expert pharmaceutical questions (Kaggle, 2× T4 GPU):

Results by Category

Results by Difficulty

Evaluation Notes

• Scoring is strict: 40% keyword matching + 60% reference answer overlap
• Model generates coherent 300-600 word answers — scores understate actual capability
• Best at decision-making (41.4%) — multi-turn reasoning data is effective
• This is a 1B model on 1K samples — a strong baseline, not the ceiling

🏋️ Training

🚀 Quick Start

Python (Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "saidutta69/RedPillV1",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("saidutta69/RedPillV1")

messages = [
    {"role": "user", "content": "Design a sustained-release formulation for metformin HCl 500mg using an HPMC matrix system."}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

output = model.generate(**inputs, max_new_tokens=1024, do_sample=False)
response = tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(response)

llama.cpp / Ollama

# Pull default quant
ollama run saidutta69/RedPillV1

# Or serve with llama.cpp
llama serve -hf saidutta69/RedPillV1

📂 Files

📦 Dataset

The RED-PILL dataset that powered this fine-tune:

from datasets import load_dataset

# Full dataset (1,047 instructions)
ds = load_dataset("saidutta69/red-pill-drug-discovery-formulation", data_files="red-pill-full.jsonl")

# Gold tier (17 grade-A instructions)
ds = load_dataset("saidutta69/red-pill-drug-discovery-formulation", data_files="red-pill-full_gold_tier.jsonl")

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

🧬 Architecture

MiniCPM5-1B (base)
  └─ Heretic abliteration (refusal removal)
      └─ LoRA fine-tuning (RED-PILL dataset)
          └─ Merged weights → Red Pill V1

• 1B parameters — runs on gaming PCs, phones, edge devices
• Heretic base — won't refuse pharmaceutical questions
• LoRA fine-tuning — domain knowledge without catastrophic forgetting
• Merged model — standalone weights, no adapter needed

⚠️ Limitations

• 1B parameters — limited reasoning for complex multi-step problems
• 1K training samples — narrow domain; more data = better performance
• English only — no multilingual support
• No real-time data — knowledge follows the base model's cutoff
• Not validated — always verify with domain experts before real-world use

📈 Future Work

🙏 Acknowledgments

• Heretic — directional ablation for uncensoring LLMs
• MiniCPM — efficient small language models
• RED-PILL Dataset — 1,047 pharmaceutical instructions
• ChEMBL, DrugBank, PubChem — open pharmaceutical databases

Made with ❤️ by RACER IS OP

Uncensored intelligence for pharmaceutical research