pharma-tinyllama-dpo-merged

pharma-tinyllama-dpo-merged

Model Summary

This is the Stage 3 merged model (final model) from the llm-finetuning-playbook pipeline.

It is produced by preference tuning (DPO) of the Stage-2 instruction-tuned model (SivaSai8143/pharma-tinyllama-instruction-merged) on prompt/chosen/rejected triples, using QLoRA (4-bit, nf4), then merging the LoRA adapter back into the base weights.

This is the final aligned model in the pipeline.

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Pipeline Position

TinyLlama-1.1B (base)
        ↓  Stage 1: Non-Instruction FT
pharma-tinyllama-non-instruction-merged
        ↓  Stage 2: Instruction FT / SFT
pharma-tinyllama-instruction-merged
        ↓  Stage 3: Preference Tuning / DPO (this model)
pharma-tinyllama-dpo-merged  ← you are here

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Training Details

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Training Data

Trained on SivaSai8143/pharma-finetuning-data (config: preference).

48 prompt/chosen/rejected triples built on the same instruction prompts as Stage 2. chosen responses are accurate domain answers; rejected responses are plausible-sounding but factually wrong or off-target answers.

{
  "prompt": "### Instruction:\\nExplain the primary mechanism of action of metformin.\\n\\n### Response:\\n",
  "chosen": "Metformin primarily acts by activating AMP-activated protein kinase (AMPK)...",
  "rejected": "Metformin mainly works by increasing insulin secretion from the pancreas..."
}

Covering:

• Metformin pharmacology, pharmacokinetics, safety & clinical use
• Lipid-lowering therapy (Atorvastatin + Ezetimibe), familial hypercholesterolemia
• mRNA vaccine platforms and immune response
• AI in drug discovery, lead optimization, ADME/toxicology
• Clinical trial terminology and pharmacovigilance

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Related Artifacts

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Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "SivaSai8143/pharma-tinyllama-dpo-merged"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

prompt = \"\"\"### Instruction:
Explain the primary mechanism of action of metformin.

### Response:
\"\"\"

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=150, do_sample=True, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Disclaimer

Educational fine-tuning project for demonstrating LLM training pipelines. The pharma content is for technical demonstration only and is not medical advice. """