pharma-tinyllama-final

Pharma TinyLlama — Final Model (Stage 3 Complete)

This is the final fully merged standalone model — the end result of a complete 3-stage fine-tuning pipeline applied to TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T:

No PEFT/LoRA library needed — load directly with 🤗 Transformers.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model     = AutoModelForCausalLM.from_pretrained("ThakrePranjal/pharma-tinyllama-final")
tokenizer = AutoTokenizer.from_pretrained("ThakrePranjal/pharma-tinyllama-final")
model.eval()

def generate(instruction, input_text="", max_new_tokens=150):
    if input_text.strip():
        prompt = (
            f"### Instruction:\n{instruction}\n\n"
            f"### Input:\n{input_text}\n\n"
            f"### Response:\n"
        )
    else:
        prompt = f"### Instruction:\n{instruction}\n\n### Response:\n"

    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    with torch.no_grad():
        out = model.generate(
            **inputs,
            max_new_tokens=max_new_tokens,
            do_sample=True,
            temperature=0.7,
            top_p=0.9,
            repetition_penalty=1.1,
            pad_token_id=tokenizer.eos_token_id,
        )
    return tokenizer.decode(out[0], skip_special_tokens=True)

# Test
questions = [
    "Explain the primary mechanism of action of metformin.",
    "Why should AI predictions in drug discovery be experimentally validated?",
    "Define pharmacovigilance.",
    "Why should atorvastatin and ezetimibe be used together?",
]
for q in questions:
    print("Q:", q)
    print("A:", generate(q))
    print()

Complete pipeline repos

Limitations

Trained on a small pharma corpus. Not validated for clinical or production use. Intended for educational/research purposes only.