Instructions to use saidutta69/RedPillV1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use saidutta69/RedPillV1.1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="saidutta69/RedPillV1.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("saidutta69/RedPillV1.1")
model = AutoModelForCausalLM.from_pretrained("saidutta69/RedPillV1.1", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use saidutta69/RedPillV1.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "saidutta69/RedPillV1.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "saidutta69/RedPillV1.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/saidutta69/RedPillV1.1

SGLang

How to use saidutta69/RedPillV1.1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "saidutta69/RedPillV1.1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "saidutta69/RedPillV1.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "saidutta69/RedPillV1.1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "saidutta69/RedPillV1.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use saidutta69/RedPillV1.1 with Docker Model Runner:
```
docker model run hf.co/saidutta69/RedPillV1.1
```

💊 Red Pill V1.1

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

Domain	What Red Pill Can Do
Drug Discovery	Hit-to-lead optimization, ADMET profiling, virtual screening strategy, SAR analysis
Formulation	Solubility enhancement (ASD, nanosizing, cyclodextrin), dosage form design, excipient selection
Synthesis	Retrosynthetic analysis, process chemistry, scale-up considerations
Regulatory	ANDA vs 505(b)(2) pathways, IND-enabling studies, FDA guidance
Decision Making	Trade-off analysis, prioritization frameworks, risk assessment
Literature	PubMed paper summarization, method critique, cross-paper synthesis

📊 Evaluation

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

Results by Category

Category	Score	Status
Decision Making	41.4%	🟢 Strong
Formulation Design	24.8%	🟡 Developing
Molecular Analysis	22.6%	🟡 Developing
Knowledge Recall	22.2%	🟡 Developing
Reasoning	15.7%	🟠 Early
Gold Tier (Discovery)	16.2%	🟠 Early
Gold Tier (Formulation)	8.9%	🔴 Needs work

Results by Difficulty

Level	Score	Questions
Advanced	22.9%	15
Intermediate	20.0%	3
Expert	14.6%	14

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

Parameter	Value
Base	MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic
Method	LoRA (r=16, α=32, dropout=0.05)
Targets	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Epochs	3
Batch	2 × 8 (gradient accumulation)
LR	2e-5 (cosine, 50 warmup steps)
Precision	fp16
Hardware	2× NvidiaTeslaT4 (14.6 GB each, Kaggle)
Time	16 min 27 sec
Loss	3.44 → 1.29 (eval, ↓62%) / 2.10 train

🚀 Quick Start

Python (Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "saidutta69/RedPillV1.1",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("saidutta69/RedPillV1.1")

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/RedPillV1.1

# Or serve with llama.cpp
llama serve -hf saidutta69/RedPillV1.1

📂 Files

File	Size	Description
`model.safetensors`	2.0 GB	Merged weights (base + LoRA)
`tokenizer.json`	9.4 MB	Tokenizer
`tokenizer_config.json`	565 B	Tokenizer config
`config.json`	749 B	Model config
`generation_config.json`	214 B	Generation settings
`chat_template.jinja`	8.9 KB	Chat template

📦 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 V1.1

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

Priority	Task	Impact
1	Extended training (10+ epochs)	↓ loss, ↑ accuracy
2	More gold-tier data (formulation)	Better formulation answers
3	Larger dataset (5K+ instructions)	Broader domain coverage
4	GGUF quantization	Ollama/llama.cpp support
5	LLM-as-judge evaluation	Fairer scoring

🙏 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

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Safetensors

Model size

1B params

Tensor type

BF16