Update README.md

README.md

@@ -1,199 +1,137 @@
 ---
 library_name: transformers
-tags: []
+license: llama3.2
 ---
 
-# Model Card for Model ID
+Certainly! Below is a draft for the README of your Hugging Face repository containing the QLoRA adapters. This README is structured to provide clear and concise information about the adapters, their purpose, and how to use them.
 
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-
-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-### Training Procedure
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
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-#### Testing Data
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-<!-- This should link to a Dataset Card if possible. -->
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-#### Factors
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-#### Metrics
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-### Results
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-#### Summary
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-
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-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
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-
-## Environmental Impact
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-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
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-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
+---
 
-#### Software
+# FineLlama-3.2-3B-Instruct-ead QLoRA Adapters
 
-[More Information Needed]
+This repository contains the QLoRA (Quantized Low-Rank Adaptation) adapters for the **FineLlama-3.2-3B-Instruct-ead** model. These adapters are designed to be used with the base [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) model to enable efficient fine-tuning for generating EAD (Encoded Archival Description) XML format for archival records.
 
-## Citation [optional]
+## Overview
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+The QLoRA adapters were trained using **Parameter Efficient Fine-Tuning (PEFT)** with LoRA (Low-Rank Adaptation) on the [Geraldine/Ead-Instruct-38k](https://huggingface.co/datasets/Geraldine/Ead-Instruct-38k) dataset. This approach allows for memory-efficient fine-tuning while maintaining high performance for the task of generating EAD/XML-compliant archival descriptions.
 
-**BibTeX:**
+### Key Features
+- **Efficient Fine-Tuning**: Uses 4-bit quantization and LoRA to reduce memory usage.
+- **Compatibility**: Designed to work with the base `meta-llama/Llama-3.2-3B-Instruct` model.
+- **Specialization**: Optimized for generating EAD/XML archival metadata.
 
-[More Information Needed]
+---
 
-**APA:**
+## Adapter Details
+
+### Training Configuration
+- **Quantization**: 4-bit quantization using `bitsandbytes`.
+  - Quantization Type: `nf4`
+  - Double Quantization: Enabled
+  - Compute Dtype: `bfloat16`
+- **LoRA Configuration**:
+  - Rank (`r`): 256
+  - Alpha (`alpha`): 128
+  - Dropout: 0.05
+  - Target Modules: All linear layers
+- **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`
 
-[More Information Needed]
+---
 
-## Glossary [optional]
+## Usage
+
+To use the QLoRA adapters, you need to load the base model and apply the adapters using the `peft` library.
+
+### Installation
+```bash
+pip install transformers torch bitsandbytes peft
+```
+
+### Loading the Model with Adapters
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+from peft import PeftModel, PeftConfig
+import torch
+
+# 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
+)
+
+# Load the base model
+base_model_name = "meta-llama/Llama-3.2-3B-Instruct"
+model = AutoModelForCausalLM.from_pretrained(
+    base_model_name,
+    quantization_config=bnb_config,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+# Load the QLoRA adapters
+adapter_model_name = "Geraldine/FineLlama-3.2-3B-Instruct-ead-Adapters"
+model = PeftModel.from_pretrained(model, adapter_model_name)
+
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+```
+
+### Example Usage
+```python
+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_tensors="pt",
+    add_generation_prompt=True
+).to(model.device)
+
+outputs = model.generate(inputs, max_new_tokens=4096, use_cache=True)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+---
 
-[More Information Needed]
+## Limitations
+- The adapters are specifically trained for EAD/XML generation and may not generalize well to other tasks.
+- Performance depends on the quality and specificity of the input prompts.
+- The maximum sequence length is limited to 4096 tokens.
 
-## More Information [optional]
+---
 
-[More Information Needed]
+## Citation
+If you use these adapters in your work, please cite the base model and this repository:
 
-## Model Card Authors [optional]
+```bibtex
+@misc{ead-llama-adapters,
+  author = {Géraldine Geoffroy},
+  title = {FineLlama-3.2-3B-Instruct-ead QLoRA Adapters},
+  year = {2024},
+  publisher = {HuggingFace},
+  journal = {HuggingFace Repository},
+  howpublished = {\url{https://huggingface.co/Geraldine/qlora-FineLlama-3.2-3B-Instruct-ead}}
+}
+```
 
-[More Information Needed]
+---
 
-## Model Card Contact
+## License
 
-[More Information Needed]
+The adapters are subject to the same license as the base [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) model. Please refer to Meta's LLaMa license for usage terms and conditions.