---
library_name: transformers
license: mit
datasets:
- Geraldine/Ead-Instruct-4k-Distilled
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
base_model:
- Qwen/Qwen2.5-0.5B-Instruct
---

# Gemini-Distill-Qwen2.5-0.5B-ead : Qwen2.5-0.5B-Instruct Fine-Tuned on EAD/XML (Distilled from Gemini-2.0-Flash-Thinking-Exp)

## Model Description
This model is a fine-tuned version of **Qwen2.5-0.5B-Instruct**, trained via knowledge distillation from **Gemini-2.0-Flash-Thinking-Exp**. The goal of this fine-tuning process is to teach the model to reason through and generate **Encoded Archival Description (EAD/XML)** outputs.

It follows a structured reasoning approach:
1. **First**, the model provides detailed reasoning.
2. **Then**, it outputs the final **EAD/XML** response.

This structure ensures that the model justifies its output before producing the archival XML format, improving interpretability and accuracy.

---
## Training Details

### **Dataset**
- Dataset: [Geraldine/Ead-Instruct-4k-Distilled](https://huggingface.co/datasets/Geraldine/Ead-Instruct-4k-Distilled)
- **Columns:**
  - `tag`: EAD/XML element
  - `prompt`: User query
  - `reasoning`: Gemini-generated reasoning traces
  - `final_output`: EAD/XML archival response
  - `completion`: Concatenation of `reasoning` and `final_output`

### **Training Process**
- **Hardware:** : NVIDIA A100-SXM4-80GB
- **Distillation Source:** Gemini-2.0-Flash-Thinking-Exp
- **Model trained on:** User prompt → Gemini reasoning traces → Final EAD/XML response
- **Tokenization Strategy:**
  - **Assistant (reasoning):** The start of the reasoning section
  - **Assistant (final answer):** The start of the XML output
  - Labels are masked (`-100`) before the reasoning phase to optimize the learning process
- **Training Hyperparameters:**
  - **Batch Size:** 4 (per device) with gradient accumulation (steps=2)
  - **Max Sequence Length:** 4096 tokens
  - **Precision:** bf16
  - **Epochs:** 5
  - **Gradient Checkpointing:** Enabled (reduces memory usage)
  - **Dataloader Efficiency:** dataloader_pin_memory=True, dataloader_num_workers=4
  - **Warmup Steps:** 100
  - **Checkpointing:** Model saved at every epoch, with a maximum of 2 saved checkpoints (save_total_limit=2)
  - **Evaluation Strategy:** Evaluates after each epoch (eval_strategy="epoch")
  - **Logging:** Logs stored in ./logs
  - **Other:** dataloader_drop_last=False to preserve all batches

This setup ensures an optimal balance between performance and memory efficiency, leveraging gradient accumulation and checkpointing for stable training on long sequences

### **Training notebook**

[https://www.kaggle.com/code/geraldinegeoffroy/ead-distilled-qwen2-5-0-5b-instruct](https://www.kaggle.com/code/geraldinegeoffroy/ead-distilled-qwen2-5-0-5b-instruct)

---
## Model Usage

### **Load Model**
To use the model with the 🤗 Transformers library:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Geraldine/Gemini-Distilled-Qwen2.5-0.5B-ead"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto", 
)
```

### **Inference Example**
```python
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Give me an example of <controlaccess> content."
messages = [
    {"role": "system", "content": "You are an archivist expert in EAD/XML format for archival records metadata."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=1024
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
```

---
## Limitations & Future Improvements
- **Training Data Size:** The dataset consists of **4,000 distilled samples**, which may limit generalization.
- **Inference Speed:** Ensure that **Sliding Window Attention (SWA) is disabled**, as it may slow down inference.
  - To disable: `model.config.sliding_window = None`
- **Potential Future Steps:**
  - Fine-tuning on larger datasets
  - Exploring LoRA/QLoRA for efficient parameter tuning

---
## Citation & Acknowledgments
If you use this model in research or production, please cite:
```
@misc{your-citation,
  author = {Géralidne Geoffroy},
  title = {Qwen2.5-0.5B-Instruct Fine-Tuned on EAD/XML},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/Geraldine/Gemini-Distill-Qwen2.5-0.5B-ead}
}
```