houck2040/tex_leg

t5-small-summarizer

This model is a fine-tuned version of the t5-small model, specifically trained for **summarizing longer text sequences. It was trained to handle inputs significantly longer than the standard T5 maximum length, aiming to process documents up to approximately 5000 tokens.

The model was trained on a custom dataset and corresponding summaries.

Model Details

• Base Model: t5-small
• Task: Text Summarization (Sequence-to-Sequence)
• Maximum Input Length during Training: 4979 tokens
• Maximum Target Length during Training: 752 tokens
• Memory Efficiency: Trained with memory-saving techniques like gradient accumulation and gradient checkpointing, suitable for environments with limited VRAM (e.g., 8GB GPU).

Intended uses & limitations

This model is intended for summarizing documents long formal texts.

Intended Uses:

• Generating concise summaries of documents.
• Experimenting with summarization of longer texts using a smaller model.

Limitations:

• Max Length: While trained on longer sequences, performance on texts exceeding the trained maximum input length (~5000 tokens) is not guaranteed.
• Compression Ratio: The model achieves a very high compression ratio (average ~100:1), meaning the generated summaries are typically very short (sentence-based). It may not be suitable for tasks requiring detailed summaries.
• Model Size: As a t5-small model, it may not capture the same level of nuance or detail as larger models.

Training Data

The model was fine-tuned on a custom dataset. This dataset consists of pairs of source texts (likely full documents or sections) and reference summaries.

Training Procedure

The model was fine-tuned using the Hugging Face transformers library Seq2SeqTrainer.

• Optimizer: adafactor
• Learning Rate: 3e-05 (Best performing)
• Weight Decay: 0.001 (Best performing)
• Effective Batch Size: Achieved through gradient_accumulation_steps=8 with a per_device_train_batch_size=1.
• Epochs: Trained for up to 3 epochs (num_train_epochs=3), with early stopping based on eval_loss.
• Gradient Checkpointing: Enabled to reduce memory usage.
• Precision: FP16 used where available.
• Hyperparameter Search: The provided code includes a hyperparameter search loop, and the uploaded model corresponds to the best configuration found.

Evaluation

The model was evaluated on a held-out test set (20% of the data) using standard summarization metrics. Evaluation included generating predictions after training was complete using the best checkpoint identified by eval_loss.

Best Model Metrics:

Metric	Value	Notes
rouge1	52.45	Unigram overlap
rouge2	36.73	Bigram overlap
rougeL	44.59	Longest Common Subsequence overlap
compression_ratio	100.09	Avg. Source Sentences / Summary Sentences * 100
bertscore_precision	89.95	Semantic precision
bertscore_recall	87.43	Semantic recall
bertscore_f1	88.63	Semantic F1
perplexity	3.8356	Target sequence fluency

Interpretation: The high BERTScore and low Perplexity indicate the model generates semantically relevant and fluent summaries. The strong ROUGE-2 score suggests good capture of key phrases. The very high compression ratio confirms the model produces very concise outputs. These are strong results for a T5-small model, particularly considering the challenge of longer input sequences.

How to Use

You can use this model directly with the Hugging Face transformers library.

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

# Replace 'your_username/your_model_name' with the actual repo ID after uploading
model_name = "your_username/"

# --- Option 1: Using the pipeline (Recommended for simplicity) ---
try:
    summarizer = pipeline("summarization", model=model_name)

    text_to_summarize = """
    [Paste a long piece of text here, up to ~5000 tokens]

    # The pipeline automatically adds the prefix and handles encoding/decoding
    summary = summarizer(text_to_summarize, max_new_tokens=150, min_new_tokens=30)[0]['summary_text']
    print("Pipeline Summary:")
    print(summary)

except Exception as e:
    print(f"Error using pipeline: {e}")
    print("Trying manual loading...")


# --- Option 2: Manual loading and generation ---
try:
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

    text_to_summarize = """
    [Paste the same long piece of text here]
    """ # Replace with your actual long text

    # Add the required prefix for T5 models
    input_text = "summarize: " + text_to_summarize

    # Tokenize the input
    # Note: max_length here should ideally match or be close to the trained max_length_source
    # for best results on long documents. Use truncation=True if inputs are longer.
    # Using return_tensors="pt" to get PyTorch tensors
    inputs = tokenizer(input_text, max_length=4979, truncation=True, return_tensors="pt")

    # Move tensors to the same device as the model (e.g., 'cuda' or 'cpu')
    device = "cuda" if torch.cuda.is_available() else "cpu"
    model.to(device)
    inputs = {k: v.to(device) for k, v in inputs.items()}

    # Generate the summary
    # max_new_tokens controls the length of the generated summary.
    # Adjust as needed, considering the trained max_length_target (752)
    # num_beams can be adjusted (e.g., 4 as used in training config)
    import torch
    with torch.no_grad():
        outputs = model.generate(
            inputs["input_ids"],
            attention_mask=inputs["attention_mask"],
            max_new_tokens=150, # Set a reasonable max length for the output summary
            min_new_tokens=30, # Set a minimum length
            num_beams=4, # Use beam search
            early_stopping=True
        )

    # Decode the generated IDs back to text
    summary_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

    print("\nManual Summary:")
    print(summary_text)

except Exception as e:
    print(f"Error using manual loading: {e}")



Efficient Long Document Summarization on Limited Hardware: A Practical Case Study

Introduction: Unlocking the Power of LLMs Beyond the Data Center
Large Language Models (LLMs) have demonstrated transformative capabilities across numerous natural language processing tasks. However, their widespread adoption and deployment face a significant hurdle: the substantial hardware requirements, particularly in terms of GPU memory (VRAM). Training and even running larger LLMs often necessitates expensive, high-end GPUs with 24GB, 40GB, or even 80GB+ of VRAM. This level of resource demand can put sophisticated AI out of reach for many individuals, small businesses, and edge computing scenarios that rely on more accessible hardware, such as consumer-grade GPUs commonly found with 8GB of VRAM or less. [1, 13, 36]
This disparity creates a critical need to develop and refine techniques that make training and utilizing powerful language models feasible on more constrained architectures. Such advancements are vital for two primary reasons: enabling the economic utilization of existing hardware infrastructure over its lifespan, and facilitating the deployment of intelligent AI directly onto edge devices where low latency and offline processing are paramount. 
While the general challenge of limited VRAM is well-recognized[1][2][3], and individual memory-saving techniques are known, there is value in demonstrating how these pieces fit together in practice for specific, challenging tasks. In this article, we present a practical case study addressing this need. We detail the process of fine-tuning a t5-small model, a relatively compact architecture (~60M parameters) , specifically for the task of summarizing long text sequences (documents up to ~5000 tokens) . Our key contribution lies in demonstrating how a combination of memory-efficient training techniques 1, 6, 9] enables the full fine-tuning (as opposed to just parameter-efficient fine-tuning methods like LoRA or QLoRA, which are also popular for limited hardware but represent a different approach)[1][4][5][6][7] of this standard model on hardware compatible with limited VRAM (specifically targeting ~8GB) 1, 13, 36], using accessible open-source data . This project serves as a proof-of-concept for making advanced NLP tasks more democratized and edge-ready by providing a concrete blueprint. 
The "Why": Driving Economic Efficiency and Enabling Edge AI
The push to make advanced AI, including LLMs, compatible with limited hardware architectures is not merely a technical exercise; it's driven by significant economic and operational necessities in the real world. Two primary forces highlight the importance of this endeavor: the need for economic utilization of existing computing infrastructure and the growing demand for intelligent processing at the network edge. 
Firstly, for many businesses and organizations, technology refresh cycles are measured in years, not months. Significant capital investments are made in computing hardware, and maximizing the return on that investment over its intended lifespan is crucial for economic sustainability. Standard data center GPUs or high-memory cloud instances capable of handling massive LLMs are often prohibitively expensive. If the only way to leverage the latest AI advancements is through constant, costly hardware upgrades, many potential adopters, particularly Small and Medium-sized Enterprises (SMEs), will be left behind. Developing methods to fine-tune and deploy sophisticated models on existing, more accessible hardware, like GPUs with 8GB or less of VRAM, allows these entities to integrate powerful AI capabilities into their operations without needing to overhaul their entire infrastructure. This approach turns existing assets into powerful tools for innovation and efficiency, directly contributing to better economic utilization and reduced operational costs. 
Secondly, the landscape of computing is increasingly moving beyond centralized data centers towards the "edge" – devices and systems operating outside traditional cloud or enterprise infrastructure. This includes a vast array of IoT devices, industrial automation systems, autonomous vehicles, and robotics. For these edge applications, low latency is often non-negotiable. Tasks like real-time object detection, immediate control responses, or on-device decision-making cannot tolerate the delays introduced by transmitting data to the cloud for processing and waiting for a response. Furthermore, many edge environments have limited or intermittent network connectivity, making cloud-dependent processing unreliable or impossible. Edge devices also face constraints in terms of power consumption and physical size, which directly limit the size and power of onboard computing components, including GPUs. Enabling sophisticated AI models, such as those capable of complex language understanding or pattern recognition, to run directly on these devices requires models that are significantly smaller, more memory-efficient, and computationally lighter than their larger counterparts. Techniques like quantization are often discussed for making large models run on 8GB for inference[2][3][8], but enabling training or fine-tuning on such devices for custom tasks requires different considerations. [2]
Consider an example like an entomological-inspired robot designed to identify and interact with objects in a field, perhaps for agricultural monitoring. Effective interaction, such as precise movement to intercept a target like a cow, is critically dependent on near-instantaneous processing of sensory data onboard the robot itself. A delay of even a fraction of a second due to transmitting data to a cloud server could result in mission failure. Training and deploying AI models that can perform complex analysis and decision-making within the power and processing constraints of the robot's embedded hardware is essential. Our work, focusing on optimizing models for limited VRAM during training, directly contributes to making this kind of real-time, onboard intelligence feasible, pushing the boundaries of what is possible at the edge. By addressing the challenges of limited hardware, we pave the way for broader AI adoption, unlock the potential of existing infrastructure, and enable a new generation of intelligent, autonomous edge applications. 
The Task and the Challenge: Summarizing Long Documents
Within this context of enabling AI on accessible hardware, our project centers on the task of text summarization – taking a longer piece of text and condensing it into a shorter, concise summary that captures the most important information. While summarization is a common application for language models[8][9][10][11][12][13], our specific focus was on handling long documents. Real-world texts, such as reports, articles, technical manuals, or indeed, legislative bills, often exceed the typical input lengths that many models or standard training setups are designed for (e.g., 512 tokens for many initial BERT-based tasks). [16, 25]
Processing documents containing thousands of tokens introduces significant technical hurdles. Sequence length is a critical factor in the memory consumption of transformer models like T5[14][15]. During training, the memory required to store intermediate activation values, calculate gradients, and manage the optimizer state grows substantially as the input sequence length increases 5, 30, 35]. This challenge is compounded when working with hardware that has limited VRAM. Even a relatively small model like t5-small, which has around 60 million parameters , can quickly exhaust the 8GB of VRAM available on many consumer GPUs when fed sequences stretching to several thousand tokens 5].
This is precisely where we aimed to push the boundaries. A core part of our approach was the deliberate decision to fine-tune the t5-small model on a dataset featuring source documents with input lengths up to approximately 5000 tokens . This was not an arbitrary choice; it was a strategic move to stress the memory capabilities of a limited VRAM environment 1] and, in doing so, necessitate the application and validation of advanced memory-saving techniques 1, 6, 9]. By confronting the challenge of long sequences head-on with a smaller model and constrained hardware, we sought to demonstrate that effective methods exist to unlock powerful NLP capabilities even under demanding conditions. The success of this fine-tuning process on such challenging data would serve as a compelling case for the viability of sophisticated AI on accessible hardware [1, 13, 36], directly supporting the goals of economic utilization and edge processing discussed previously. 
The Data: Leveraging Open-Source Legislative Text
A crucial element in any fine-tuning project is the dataset. To train our t5-small model for long document summarization , we required a collection of paired long texts and their corresponding summaries. Aligning with our goal of demonstrating accessible AI development, we intentionally chose to leverage publicly available, open-source data from the Texas legislature website. 
Using this open-source data provided several key advantages. Firstly, it eliminated the need for costly licenses or access fees often associated with large, curated datasets, thereby reducing a significant economic barrier to entry . The use of open datasets is a common practice in making AI research and development more accessible [24]. Anyone can potentially access and utilize this public information, fostering reproducibility and enabling others to build upon this work. Secondly, legislative texts are inherently formal, complex, and frequently very long, providing an ideal testbed for our goal of handling lengthy sequences (up to the ~5000 tokens we targeted) . The dataset consists of legislative bills that passed and their official summaries, providing the necessary input-output pairs for training a summarization model. 
While the data is publicly available, acquiring it was a practical exercise in data collection. Navigating the website structure, identifying the links to the full 'Enrolled Bill' text and the official 'Bill Summary', and extracting the content required the application of web scraping techniques using libraries like BeautifulSoup and Selenium. This process, while sometimes challenging due to the website's structure and needing careful handling to avoid overwhelming the server, ultimately yielded a valuable dataset tailored to our specific task of summarizing long, domain-specific documents. 
The use of this real-world, open-source dataset of Texas legislative bills  not only provided the necessary material for fine-tuning but also strongly reinforces the core message of this article: that powerful AI applications can be developed using accessible resources – both in terms of the computing hardware [1, 13, 36] and the data used for training [24]. This domain-specific data allows the fine-tuned model to become particularly adept at handling formal, legislative language, showcasing the potential for creating highly relevant AI tools for specific industries or use cases without requiring proprietary data lakes or massive computational budgets. 
The "How": Training on Limited Hardware (Technical Deep Dive)
Successfully fine-tuning a transformer model like T5-small  to process input sequences up to approximately 5000 tokens  on a GPU with limited VRAM (representative of many consumer-grade cards around the 8GB mark) [1, 13, 36] presented a significant technical challenge. As discussed, long sequences drastically increase memory consumption during training due to the need to store activations, gradients, and optimizer states [5, 30, 35]. Our approach was deliberately designed to address this head-on by employing a combination of well-established memory-efficient techniques available through the Hugging Face ecosystem[1][16][17].
We utilized the Hugging Face transformers library and its Seq2SeqTrainer, which provides convenient implementations of these crucial optimizations[1][16][18]. The specific training arguments were configured to prioritize memory efficiency, enabling the process to fit within the ~8GB VRAM limit while handling the long input sequences . Here’s a breakdown of the key strategies employed:

Gradient Accumulation: Training often benefits from larger effective batch sizes for better convergence. However, a large batch size requires substantial memory. With limited VRAM, a standard batch size might be impossible. To overcome this, we set the per_device_train_batch_size to 1 . To achieve the effect of a larger batch, we used gradient_accumulation_steps=8 . The trainer accumulates gradients over 8 steps with a batch size of 1 before performing an optimizer step, simulating an effective batch size of This requires memory only for a single example's gradient computation at a time[17]. This is a standard technique for VRAM-constrained training[1][16][17][19].
# Configured within Seq2SeqTrainingArguments
per_device_train_batch_size=1,
gradient_accumulation_steps=8,

Gradient Checkpointing: The backward pass requires storing intermediate activation values from the forward pass, which consume significant memory for long sequences 5, 30]. Gradient Checkpointing (gradient_checkpointing=True)  addresses this by trading computation time for memory. Instead of storing all activations, it stores only a few and recomputes others during the backward pass, reducing the memory peak during backpropagation[16][17][20][21]. This is a widely used memory-saving technique[1][16][17][20][21].
# Configured within Seq2SeqTrainingArguments
gradient_checkpointing=True,

Mixed Precision Training (FP16): Standard training uses 32-bit floating-point numbers (FP32). Mixed precision training utilizes a combination of FP32 and 16-bit floating-point numbers (FP16). By performing most calculations and storing tensors in FP16, memory usage is significantly reduced[14]. We enabled this using fp16=True (conditional on CUDA availability) . This is a common practice for reducing memory and speeding up training on compatible hardware[1][14][22][23].
# Configured within Seq2SeqTrainingArguments
fp16=torch.cuda.is_available(),

Adafactor Optimizer: The optimizer also stores state information, which can consume significant memory, especially for large models[24][25]. Adafactor (optim="adafactor") is an optimizer known for being more memory-efficient than traditional AdamW, which is beneficial for large models or constrained memory environments.
# Configured within Seq2SeqTrainingArguments
optim="adafactor",

Adjusted Evaluation and Generation Batch Sizes: Even during evaluation or prediction generation, processing long sequences can be memory-intensive 5, 35]. We specifically lowered the batch sizes used during evaluation (per_device_eval_batch_size=2)  and within metric calculation functions (like BERTScore, using a batch size of 4)  to prevent out-of-memory errors even in these phases[26][27]. This highlights the need for memory considerations throughout the entire model lifecycle on limited hardware. 

By combining these techniques – simulating larger batches with gradient accumulation 1, 6, 9, 10], reducing activation memory with gradient checkpointing 1, 6, 9, 38, 45], halving memory usage with FP16 1, 21, 35, 40], employing a memory-efficient optimizer , and adjusting batch sizes during evaluation 47, 48] – we were able to successfully fine-tune the t5-small model on a dataset with challenging long input sequences , proving that sophisticated NLP tasks can be achieved on hardware previously thought insufficient 1, 13, 36]. While Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA and QLoRA are powerful alternatives often used for limited resources[1][4][5][6][7][28][29], our case study demonstrates that applying these core memory-saving techniques for standard fine-tuning is also a viable and effective strategy. 
Note: The full training script, including detailed configurations and hyperparameter search logic, is available in the model repository for those interested in replicating or adapting the process. 
Performance and Evaluation Results
Achieving successful training on limited hardware is only one part of the equation; the fine-tuned model must also perform well on the intended task. To evaluate the quality of the summaries generated by our t5-small-summarizer , we assessed its performance on a held-out test set (20% of the data)  using standard metrics commonly used for summarization tasks[13][30][31]. These metrics provide insight into the overlap of words/phrases, semantic similarity, and fluency of the generated summaries compared to the reference summaries in our dataset. 
The evaluation was performed using the best checkpoint saved during the hyperparameter search, selected based on the lowest evaluation loss . The key metrics achieved by this model configuration are as follows: 
Metric
Value
Interpretation
rouge1
52.45
Good unigram overlap with reference summaries.
rouge2
36.73
Strong bigram overlap, indicating capture of key phrases.
rougeL
44.59
Good capture of the longest common subsequence, reflecting overall summary structure. 
compression_ratio
100.09
Very high sentence-based compression (avg ~100 source sentences per 1 summary sentence). 
bertscore_precision
89.95
Excellent semantic precision (generated tokens are relevant to reference). 
bertscore_recall
87.43
Excellent semantic recall (reference meaning is well-captured by generated text).
bertscore_f1
88.63
Excellent overall semantic similarity. 
perplexity
3.8356
Outstanding fluency and coherence of generated summaries. 

Interpretation of Results:
The ROUGE scores indicate a strong overlap with the reference summaries 14, 31]. The high BERTScore metrics demonstrate that the generated summaries are also semantically close to the human-written references 2, 14, 15, 31, 47]. Perplexity, being exceptionally low, suggests high confidence and fluency in the generated text . The very high compression ratio confirms the model's ability to condense information drastically .
These results are strong, especially considering the base model size (t5-small - ~60M parameters) , the challenge of processing input sequences up to ~5000 tokens , and the memory constraints under which the training was performed (~8GB VRAM compatible hardware) 1, 13, 36]. They demonstrate that the combination of a suitable base model 43], a relevant open-source dataset , and carefully applied memory-saving techniques 9, 10] can yield an effective model for a challenging, real-world task, even on hardware with limited VRAM. The model successfully learns to summarize long documents while remaining within the practical constraints necessary for economic utilization and potential edge deployment. 
Implications and Future Directions
The successful fine-tuning of a t5-small model for summarizing long documents (up to ~5000 tokens) using memory-efficient techniques on hardware with limited VRAM 1, 2, 4, 5, 6, 7, 8, 10, 13, 36] offers significant implications for the accessibility and deployment of sophisticated AI. This project serves as a concrete case study demonstrating that powerful natural language processing capabilities are not solely the domain of research labs or large corporations with access to massive computational resources. 
By proving that a challenging task like long document summarization  can be effectively tackled on infrastructure compatible with, for instance, an 8GB GPU 1, 13, 36], we directly address the need for economic utilization of existing hardware. Businesses and individuals can leverage this approach to gain valuable insights from their long-form text data – such as internal reports, technical documentation, or legal texts – without the prohibitive cost of constant hardware upgrades or expensive cloud-based processing for every document. This democratizes access to powerful AI tools, enabling wider adoption and innovation across various sectors. 
Furthermore, this work is a crucial step towards enabling Edge AI. The ability to train and utilize models that are optimized for smaller memory footprints and computational efficiency 9, 10] is fundamental for deploying intelligence directly onto devices operating at the network edge. While our project focused on summarizing legislative text , the underlying methodology – fine-tuning a compact model (t5-small)  with memory-saving techniques 9, 10] to handle complex, long-sequence data  – is directly applicable to developing capabilities for edge devices. Imagine IoT sensors summarizing logs locally, robots interpreting text-based commands or documentation onboard, or edge gateways processing data streams for real-time analysis. A model fine-tuned like ours provides the kind of efficient, capable processing needed for such low-latency, offline edge applications. [2]

This project opens several exciting avenues for future work:

Exploring Advanced PEFT Methods: While we used fundamental memory-saving techniques during full model fine-tuning , exploring Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA or QLoRA[1][4][5][6][7][28][29] could potentially allow fine-tuning even larger base models (like T5-base or even T5-large) on similar limited hardware by training only a small percentage of parameters. This could lead to models with greater capacity or nuance while retaining hardware accessibility[1][4][5][6][7].

Optimizing Inference: Although T5-small is relatively efficient for inference and should fit on 8GB VRAM [13, 36], further techniques like advanced quantization (e.g., 4-bit) specifically for inference could reduce the memory footprint even further, making deployment possible on an even broader range of edge devices or enabling larger models for inference on 8GB[2][3][8].

Applying to Other Domains and Tasks: The methodology demonstrated here  could be applied to other long-sequence NLP tasks (like question answering over documents, information extraction from long texts, or long-form content generation) and adapted for different domains using relevant open-source datasets. 
How to Use the Model
One of the significant advantages of using the Hugging Face ecosystem is the ease with which models can be shared and utilized [3, 4, 8, 19, 24]. Our fine-tuned t5-small-summarizer model, trained on Texas legislative data , is designed to be readily accessible through the transformers library.
Once the model is uploaded to the Hugging Face Hub (you'll replace "your_username/your_model_name" with the actual repository ID), you can easily load and use it for summarizing your own long documents. 
Here are two common ways to use the model. Note that the full code for training and detailed inference is available in the model's repository for reference. 
1. Using the pipeline (Recommended for Simplicity)
The pipeline abstraction in the transformers library handles tokenization, model inference, and decoding. [3, 4, 8, 19, 24]
from transformers import pipeline
import torch

# Replace 'your_username/your_model_name' with your repo ID
model_name = "your_username/your_model_name"

try:
    # Initialize pipeline on GPU (0) if available, otherwise CPU (-1)
    summarizer = pipeline("summarization", model=model_name, device=0 if torch.cuda.is_available() else -1)

    text_to_summarize = """
    [Paste your long document text here, up to ~5000 tokens.]
    """

    # Generate summary (adjust max/min_new_tokens as needed)
    summary = summarizer(text_to_summarize, max_new_tokens=150, min_new_tokens=30)[0]['summary_text']

    print("Generated Summary:")
    print(summary)

except Exception as e:
    print(f"Error: {e}")
    print("Ensure libraries are installed and model name is correct.")

```*Note: Install `transformers`, `torch`, and `accelerate` (`pip install transformers torch accelerate`).*

**2. Manual Loading and Generation**

For more control, manually load the tokenizer and model.

```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

# Replace 'your_username/your_model_name' with your repo ID
model_name = "your_username/your_model_name"

try:
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device).eval()

    text_to_summarize = """
    [Paste your long document text here.]
    """
    input_text = "summarize: " + text_to_summarize

    # Tokenize (using trained max_length)
    inputs = tokenizer(input_text, max_length=4979, truncation=True, return_tensors="pt") # 
    inputs = {k: v.to(device) for k, v in inputs.items()}

    # Generate summary (adjust params like num_beams)
    with torch.no_grad():
        outputs = model.generate(
            input_ids=inputs["input_ids"],
            attention_mask=inputs["attention_mask"],
            max_new_tokens=150,
            min_new_tokens=30,
            num_beams=4, # 
            early_stopping=True
        )

    summary_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    print("\nManual Summary:")
    print(summary_text)

except Exception as e:
    print(f"Error: {e}")
    print("Ensure libraries are installed and model name is correct.")
    print("If CUDA OOM occurs, try reducing max_new_tokens or batch size.")

By making the model available on the Hugging Face Hub and providing these simple code examples, we enable anyone to easily experiment with and utilize a model capable of tackling challenging long document summarization tasks on accessible hardware. [3, 4, 8, 19, 24]
Conclusion: Towards More Accessible and Deployable AI
The rapid advancements in Large Language Models present immense opportunities, but the significant hardware demands associated with them have created a bottleneck for widespread accessibility and deployment, especially on less powerful or existing infrastructure. The need to economically utilize current computing resources and to enable intelligent processing directly on edge devices highlights a critical gap that must be addressed. 
In this article, we presented a practical case study focusing on fine-tuning a t5-small model for summarizing long documents (up to ~5000 tokens)  – a task specifically chosen to challenge the memory limits of training on hardware with limited VRAM (such as an 8GB GPU) [1, 2, 4, 5, 6, 7, 8, 10, 13, 36]. By strategically employing a combination of well-established memory-saving techniques such as Gradient Accumulation, Gradient Checkpointing, and FP16 mixed precision training 1, 3, 5, 9, 10], combined with leveraging a valuable open-source dataset of Texas legislative bills , we demonstrated that it is indeed possible to achieve effective performance on complex NLP tasks under significant hardware constraints.

The evaluation results, showing strong ROUGE and BERTScore metrics alongside low perplexity and a high compression ratio , validate that our approach successfully produced a capable summarization model despite the limitations of model size and training environment. 
This project serves as a tangible example and a step forward in the crucial effort to make powerful AI more accessible and deployable. It underscores that focused fine-tuning and careful application of memory-efficient methods can unlock sophisticated capabilities on hardware that is readily available to a much wider audience than high-end data center GPUs. By providing a practical blueprint for applying these techniques to full fine-tuning of a standard model for long-sequence processing on limited VRAM , and by reducing the barriers related to both computational cost and data accessibility (through the use of open-source data) , we contribute to the democratization of AI development and pave the way for new applications in economically constrained environments and at the network edge [2].
Continued research and development in optimizing models and training techniques for limited architectures will be vital in ensuring that the transformative power of LLMs can be harnessed by everyone, everywhere. 


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