SCCSMARTCODE
/

finetuned-gemma2b-lora

@@ -1,201 +1,170 @@
 ---
-library_name: transformers
 tags:
-- trl
-- sft
 ---
-# Model Card for Model ID
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
+license: apache-2.0
 tags:
+- transformers
+- gemma
+- causal-lm
+- qlora
+- peft
+- chatml
+library_name: transformers
+language: en
+pipeline_tag: text-generation
+datasets:
+- Abirate/english_quotes
+base_model:
+- google/gemma-2-2b
+new_version: SCCSMARTCODE/finetuned-gemma2b-lora
 ---
+# Fine-Tuned Gemma-2B with QLoRA on English Quotes (Author & Tags Prediction)
+This model is a fine-tuned version of **[`google/gemma-2-2b`](https://huggingface.co/google/gemma-2-2b)**, using **QLoRA** and **PEFT (LoRAConfig)** techniques to train on a conversational version of the [`Abirate/english_quotes`](https://huggingface.co/datasets/Abirate/english_quotes) dataset.
+The goal is to predict the **author** and **tags** of a quote, formatted using **ChatML-style prompts**, making it suitable for lightweight conversational applications or metadata generation.
+---
+## ✨ Model Summary
+- **Base model:** [`google/gemma-2-2b`](https://huggingface.co/google/gemma-2-2b)
+- **Parameter-efficient fine-tuning:** LoRA (r=64, alpha=16, dropout=0.1)
+- **Quantization:** 4-bit QLoRA (via BitsAndBytes)
+- **Training Data:** 2,000 English quotes with author + tags
+- **Prompt format:** ChatML (multi-turn)
+- **Language:** English
+- **Model type:** Decoder-only causal LM
+- **License:** [Gemma Terms of Use](https://ai.google.dev/gemma/terms)
+---
+## 🧠 How It Works
+Each training example was transformed into the following **ChatML format**:
+```
+\<start\_of\_turn>user
+"Be yourself; everyone else is already taken."
+\<end\_of\_turn>
+\<start\_of\_turn>model
+Author: Oscar Wilde
+Tags: inspirational, self, identity
+\<end\_of\_turn>
+````
+The model learns to generate structured metadata in a natural language response.
+---
+## 📦 How to Use
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+model_id = "SCCSMARTCODE/finetuned-gemma2b-lora"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
+tokenizer.pad_token = tokenizer.eos_token
+tokenizer.padding_side = "right"
+prompt = (
+    "<start_of_turn>user\n"
+    "“Be yourself; everyone else is already taken.”\n"
+    "<end_of_turn>\n"
+    "<start_of_turn>model\n"
+)
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=32)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+````
+---
+## 🗂️ Dataset
+* **Name:** [`Abirate/english_quotes`](https://huggingface.co/datasets/Abirate/english_quotes)
+* **Fields used:** `"quote"`, `"author"`, `"tags"`
+* **Size:** 2,000 examples used for fine-tuning
+---
+## 🏋️ Training Details
+* **Frameworks:** Transformers, TRL, PEFT, BitsAndBytes
+* **Compute:** Colab T4 / A100 (mixed precision)
+* **Epochs:** 1
+* **Batch size:** 1 (with gradient accumulation = 16)
+* **Optimizer:** `paged_adamw_8bit`
+* **LR scheduler:** Cosine
+* **Learning rate:** 2e-4
+* **Mixed precision:** fp16
+* **Quantization:** 4-bit via QLoRA (bnb\_4bit)
+---
+## 📈 Intended Use
+### ✅ Direct Use
+* Conversational agents generating metadata for quotes
+* Training demos for QLoRA + LoRA on limited compute
+* Style-aligned structured generation in lightweight applications
+### 🚫 Out-of-Scope Use
+* Any high-stakes decision-making
+* Factual attribution in academic or legal domains
+* Non-English quote metadata extraction
+---
+## ⚠️ Bias, Risks & Limitations
+* **Cultural bias:** Author predictions are based on dataset exposure and may reflect selection bias.
+* **Dataset limitations:** Author/tag mappings are not always consistent or exhaustive.
+* **Small scale:** The model was trained on a small subset (2,000 samples), which limits generalization.
+---
+## 🧪 Evaluation
+Informal evaluation shows the model correctly extracts authors/tags for known quotes, but performance may degrade for rare or noisy examples.
+---
+## 🧾 Citation
 **BibTeX:**
+```bibtex
+@misc{gemma-quotes-sft,
+  author = {Emmanuel Ayobami Adewumi},
+  title = {Fine-Tuned Gemma-2B on English Quotes for Author and Tag Prediction},
+  year = 2025,
+  howpublished = {\url{https://huggingface.co/your-username/fine-tuned-gemma-quotes}},
+  note = {Fine-tuned using QLoRA + PEFT}
+}
+```
+---
+## 🙋 Contact
+Created by **Emmanuel Ayobami Adewumi**
+For questions or feedback, reach out on [Hugging Face](https://huggingface.co/SCCSMARTCODE) or [GitHub](https://github.com/SCCSMARTCODE)
+---
+## 🏁 Future Work
+* Expand dataset to 10k+ quotes for better generalization
+* Add author style generation (not just metadata)
+* Serve on Gradio with editable quote inputs
+---