Geraldine
/

FineLlama-3.2-3B-Instruct-ead

@@ -1,199 +1,147 @@
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 ---
 library_name: transformers
+license: llama3.2
 ---
+# FineLlama-3.2-3B-Instruct-ead
+This repository contains a fine-tuned version of LLaMa-3.2-3B-Instruct specifically trained to understand and generate EAD (Encoded Archival Description) XML format for archival records description.
+## Model Description
+* **Base Model**: meta-llama/Llama-3.2-3B-Instruct
+* **Training Dataset**: [Geraldine/Ead-Instruct-38k](https://huggingface.co/datasets/Geraldine/Ead-Instruct-38k)
+* **Task**: Generation of EAD/XML compliant archival descriptions
+* **Training Type**: Instruction fine-tuning with PEFT (Parameter Efficient Fine-Tuning) using LoRA
+## Key Features
+* Specialized in generating EAD/XML format for archival metadata
+* Trained on a comprehensive dataset of EAD/XML examples
+* Optimized for archival description tasks
+* Memory efficient through 4-bit quantization
 ## Training Details
+### Technical Specifications
+* **Quantization**: 4-bit quantization using bitsandbytes
+  * NF4 quantization type
+  * Double quantization enabled
+  * bfloat16 compute dtype
+### LoRA Configuration
+```
+- r: 256
+- alpha: 128
+- dropout: 0.05
+- target modules: all-linear
+```
+### Training parameters
+```
+- Epochs: 3
+- Batch size: 3
+- Gradient accumulation steps: 2
+- Learning rate: 2e-4
+- Warmup ratio: 0.03
+- Max sequence length: 4096
+- Scheduler: Constant
+```
+### Training Infrastructure
+* Libraries: transformers, peft, trl
+* Mixed Precision: FP16/BF16 (based on hardware support)
+* Optimizer: fused adamw
+### Training Notebook
+The training Notebook is available on [Kaggle](https://www.kaggle.com/code/geraldinegeoffroy/ead-finetune-llama-3-2-3b-instruct)
+## Usage
+### Installation
+```
+pip install transformers torch bitsandbytes
+```
+### Loading the model
+```
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+import torch
+from peft import PeftModel, PeftConfig
+# Configure 4-bit quantization
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16
+)
+model_name = "Geraldine/FineLlama-3.2-3B-Instruct-ead"
+# Load model and tokenizer
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    quantization_config=bnb_config
+).to("cuda")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+```
+### Example usage
+```
+messages = [
+  {"role": "system", "content": "You are an expert in EAD/XML generation for archival records metadata."},
+  {"role": "user", "content": "Generate a minimal and compliant <eadheader> template with all required EAD/XML tags"},
+]
+inputs = tokenizer.apply_chat_template(
+    messages,
+    return_dict=True,
+    tokenize = True,
+    add_generation_prompt = True, # Must add for generation
+    return_tensors = "pt",
+).to("cuda")
+outputs = model.generate(**inputs,
+                         max_new_tokens = 4096,
+                         pad_token_id=tokenizer.eos_token_id,
+                         use_cache = True,)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+## Limitations
+* The model is specifically trained for EAD/XML format and may not perform well on general archival tasks
+* Performance depends on the quality and specificity of the input prompts
+* Maximum sequence length is limited to 4096 tokens
 ## Citation [optional]
 **BibTeX:**
+```
+@misc{ead-llama,
+  author = {Géraldine Geoffroy},
+  title = {EAD-XML LLaMa: Fine-tuned LLaMa Model for Archival Description},
+  year = {2024},
+  publisher = {HuggingFace},
+  journal = {HuggingFace Repository},
+  howpublished = {\url{https://huggingface.co/Geraldine/FineLlama-3.2-3B-Instruct-ead}}
+}
+```
+## Licence
+This model is subject to the same license as the base LLaMa model. Please refer to Meta's LLaMa license for usage terms and conditions.