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--- |
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language: |
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- en |
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tags: |
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- autotrain |
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- text-generation |
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- causal-lm |
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- lora |
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- style-transfer |
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- agatha-christie |
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license: other |
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datasets: |
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- realdanielbyrne/AgathaChristieText |
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base_model: google/gemma-2-2b-it |
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library_name: transformers |
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pipeline_tag: text-generation |
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--- |
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# 🕵️ Agatha Christie Style LoRA — Gemma-2-2B-IT |
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This model is a **LoRA fine-tuned** version of **Google’s Gemma-2-2B-IT**, trained to generate prose in the **classic style of Agatha Christie**. |
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It captures the tone, pacing, domestic atmosphere, and subtle psychological tension characteristic of Christie’s detective fiction. |
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The model is intended for **story continuation**, **creative writing**, and **mystery scene generation**. |
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It is a **causal LM**, not an instruction-tuned chat model. |
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--- |
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## ✨ Model Details |
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- **Model type:** Causal language model (LoRA adapter) |
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- **Base model:** `google/gemma-2-2b-it` |
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- **Fine-tuning method:** LoRA (PEFT) |
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- **Training framework:** Hugging Face AutoTrain |
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- **Languages:** English |
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- **Primary domain:** Fiction, mystery, detective prose |
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- **Task:** Text generation (story continuation) |
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--- |
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## 🎯 Intended Use |
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This model is suitable for: |
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- Generating text in the Agatha Christie style |
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- Continuing mystery scenes |
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- Creative writing assistance |
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- Prototyping detective stories |
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- Experimenting with stylistic transfer |
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### Not recommended for: |
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- Real-world Q&A |
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- Safety-critical applications |
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- Factual reasoning |
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- Chat-assistant behavior without custom prompting |
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--- |
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## 📚 Training Data |
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The model was trained on: |
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- **`realdanielbyrne/AgathaChristieText`** |
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A plain-text dataset containing ~13,000 rows of Agatha Christie prose. |
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No instruction template was used; the model was trained purely on **unstructured text** to learn style rather than task formats. |
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--- |
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## 🔧 Training Configuration |
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- **Epochs:** 3 |
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- **Block size:** 1024 |
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- **Learning rate:** 3e-5 |
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- **Batch size:** AutoTrain default |
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- **Optimizer:** AdamW |
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- **Chat template:** *None (causal LM)* |
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- **LoRA target modules:** Linear projection layers of Gemma |
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Training was performed using **AutoTrain**, which handled preprocessing, batching, and evaluation. |
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--- |
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## 🧠 Model Behavior |
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The model tends to produce: |
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- restrained, elegant British prose |
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- domestic settings and quiet tension |
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- subtle descriptions of character behavior |
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- slow, methodical narrative pacing |
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- attention to objects, rooms, and small clues |
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### Example characteristics: |
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- No supernatural or horror tone unless explicitly prompted |
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- Minimal modern slang |
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- High coherence for 1–3 paragraphs of continuation |
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- Strong stylistic adherence to Christie’s sentence rhythm |
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--- |
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## ⚠️ Limitations and Biases |
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- May repeat narrative structures due to small dataset size |
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- Not optimized for long-range story consistency |
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- Inherits biases present in Agatha Christie’s historical works |
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- Not suited for factual or logical reasoning |
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- Can generate outdated cultural norms reflective of 20th-century British literature |
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--- |
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## 📊 Evaluation |
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This model was not evaluated using standard NLP benchmarks, as its purpose is **stylistic generation** rather than accuracy on downstream tasks. |
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Qualitative evaluation shows: |
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- Strong stylistic similarity to Christie’s prose |
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- High coherence in short continuations |
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- Appropriate tone, atmosphere, and pacing |
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--- |
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## 🔐 License |
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This LoRA inherits licensing constraints from: |
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- the **Google Gemma-2** gated model license |
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- the dataset used |
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- Hugging Face AutoTrain’s output terms |
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Users must be approved to access the Gemma model family. |
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--- |
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## 🙏 Acknowledgements |
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- Base model by **Google DeepMind** |
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- Fine-tuned using **Hugging Face AutoTrain** |
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- Dataset contributed by the open-source community |
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- PEFT LoRA framework by **Hugging Face** |
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--- |
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## 📣 Citation |
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If you use this model, please cite the base model and the dataset authors. |
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You may also reference this repository. |
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