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Model: tweijie/tinyllama-medical-merged
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
+tags:
+  - text-generation
+  - lora
+  - peft
+  - merged
+  - medical
+  - fine-tuned
+datasets:
+  - BI55/MedText
+language:
+  - en
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# TinyLlama-1.1B — Medical QLoRA (Merged)
+
+A QLoRA fine-tuned variant of [TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T), trained on medical question-answering data. The LoRA adapter has been merged into the base weights, so this repo is a standalone model — no separate adapter loading required.
+
+## ⚠️ Intended use
+
+This model was trained as part of a **GenAI training lab** to demonstrate the fine-tuning → merge → deploy workflow. It is **not validated for real clinical or medical use** and should not be used to inform actual medical decisions. Treat its outputs as educational examples of what a small fine-tuned LLM can produce, not medical advice.
+
+## Training details
+
+- **Base model:** `TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T`
+- **Method:** QLoRA (4-bit NF4 quantization during training) via `peft` + `trl.SFTTrainer`
+- **LoRA config:** `r=8`, `lora_alpha=16`
+- **Dataset:** [`BI55/MedText`](https://huggingface.co/datasets/BI55/MedText) — medical case/question-answer pairs
+- **Steps:** 400
+- **Merge:** adapter merged into the base model via `PeftModel.merge_and_unload()`, then saved as a full standalone model
+
+## How to use
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+repo_id = "tweijie/tinyllama-medical-merged"
+
+model = AutoModelForCausalLM.from_pretrained(repo_id)
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_length=200)
+prompt = "<s>[INST] A patient presents with restless leg sensations at night. What could be the cause? [/INST]"
+print(pipe(prompt)[0]["generated_text"])
+
+
+Limitations
+1.1B parameters — small model, prone to hallucination and factual errors, especially on medical content.
+Fine-tuned on a single narrow dataset (400 training steps); not broadly evaluated.
+No safety/alignment tuning beyond the base model's own training.