When you implement a custom Large Language Model (LLM), it’s important to understand the specific use cases you can apply it to, as each has distinct performance metrics that reveal how well a model can solve a particular task.
You can use an LLM for text classification to assign predefined labels to text for tasks like sentiment analysis and topic modeling. The performance of LLMs in this area can be significantly enhanced through fine-tuning. For example, a Llama-3-8B model’s accuracy on spam SMS detection jumped from 39% to 98% after being fine-tuned[1]. Similarly, a fine-tuned Qwen-7B model saw its accuracy improve by up to 38.6% on certain datasets[1]. For complex tasks like multiclass classification, models such as Llama3 and GPT-4 can outperform traditional machine learning methods[2].
You can leverage LLMs to create concise summaries of long documents. While traditional metrics like ROUGE exist, they often show a low correlation with human judgments on the quality of LLM-generated summaries[3]. Newer evaluation methods use another LLM to assess summaries based on key dimensions like completeness, correctness, and readability[3]. Research indicates that LLM-based evaluations align more closely with human assessments compared to older automated metrics[4].
You can utilize LLMs for powerful machine translation, and recent models have shown superior performance over traditional systems. In a blind comparison study, LLMs like Claude 3.5-Sonnet and GPT-4o were rated as producing “good” quality translations more often than dedicated services like Google Translate and DeepL[5]. Claude 3.5-Sonnet was the top performer, achieving good translations in about 78% of cases across several languages[5]. These findings were validated at the WMT24 conference, where Claude 3.5-Sonnet was the best system in 9 of 11 language pairs[5]. Furthermore, fine-tuning can dramatically boost performance; a 13B LLaMA-2 model fine-tuned for translation (ALMA-13B) saw its performance increase by an average of 12 BLEU points, outperforming GPT-3.5[6].
To build advanced AI systems that can access your private data, you can use Retrieval-Augmented Generation (RAG). This technique significantly improves factual accuracy. A Stanford study found that without RAG, LLMs answered questions with only 34.7% accuracy, but with access to correct reference documents via RAG, their accuracy soared to 94%[7]. Similarly, when answering questions from enterprise SQL databases, an LLM’s accuracy improved from 16.7% to 54.2% when it could access a knowledge graph representation of the data[8].
You can use LLMs to create original written material, from professional emails to marketing copy. The quality of this generated content is measured using a variety of metrics that assess its relevance, coherence, and factual accuracy[9]. Key evaluation criteria include:
If you are a developer, you can use LLMs as coding assistants. Performance is often measured with the pass@k metric, which calculates the probability that at least one of k generated code samples is correct. Performance varies by model size and capability; a smaller model might achieve a pass@1 of 27% and a pass@5 of 66%, while a state-of-the-art model like GPT-4o can achieve a pass@1 of 90.2% on the HumanEval benchmark[10]. In more specific tasks, such as implementing software design patterns, LLMs have demonstrated high success rates, ranging from 84% for the Observer pattern to 94% for the Strategy pattern[11].
You can automate data entry by using an LLM to pull structured information from unstructured text. In this domain, LLMs can achieve very high accuracy. For instance, models like Claude-3.5-sonnet have reached an accuracy of 96.2% on certain data extraction tasks[12]. Performance can be further improved with a human-in-the-loop approach; one study found that an LLM-only method achieved 95.7% accuracy, but an LLM-assisted method where a human validates the output increased accuracy to 97.3%[12].
You can power modern chatbots and virtual assistants with LLMs. The effectiveness of these conversational agents is evaluated using a multi-dimensional approach that goes beyond simple accuracy[13]. Key metrics include:
When developing a custom LLM, defining the type of data it will process is crucial, as performance varies significantly across domains.
LLMs trained on general-purpose data are designed to handle a wide array of topics. The quality of this training data is mission-critical, as it directly impacts the accuracy, reliability, and generalization capabilities of the model [15]. Performance is often measured using broad benchmarks like MMLU (Massive Multitask Language Understanding), which assesses knowledge across 57 subjects. An empirical formula called the “Performance Law” can even predict a model’s MMLU score based on its parameters [16]. However, it’s important to recognize that general performance benchmarks can be unsatisfying because the range of potential use cases is vast and difficult to capture in a single metric [17]. A fundamental way to evaluate performance is by calculating the cross-entropy loss or negative log likelihood on a dataset, which measures how well the model predicts the next token in a sequence [18].
Mathematics presents a unique challenge for LLMs due to its demand for precision and structured reasoning. When evaluated on questions from the Math Stack Exchange, GPT-4 demonstrated the best performance among several models with a normalized discounted cumulative gain (nDCG) of 0.48 and a Precision@10 of 0.37, though it still failed to provide consistently accurate answers for more complex problems [19][20]. The quality of pre-training data is a key factor. By systematically rewriting and refining mathematical training datasets, a Llama-3.1-8B model’s accuracy improved by +12.4 on the GSM8K benchmark and +7.6 on the MATH benchmark [21].
LLMs are increasingly used for code generation, and their performance can be measured by their ability to produce efficient and correct code. In a study comparing 18 LLMs on Leetcode problems, researchers found that models could generate code that was, on average, more efficient than solutions written by humans [22]. Another controlled experiment evaluated leading models on data science coding challenges, revealing that ChatGPT and Claude had the highest success rates, particularly in analytical and algorithmic tasks [23]. The quality of training data is also paramount for coding. Continual pre-training with a refined codebase (SwallowCode) allowed a Llama-3.1-8B model to boost its pass@1 score by +17.0 on the HumanEval benchmark, demonstrating a significant improvement in code generation capability [21].
The legal field requires a high degree of precision and domain-specific knowledge. To measure LLM capabilities in this area, the LawBench benchmark was created. It assesses models on three levels: legal knowledge memorization, understanding, and application across 20 different tasks [24][25]. In an extensive evaluation of 51 different LLMs, GPT-4 was found to be the best-performing model by a significant margin [24][25]. Another key metric is factuality. Research shows that fine-tuning a model on legal-specific documents can improve its factual precision from 63% to 81% when answering questions about case law and legislation [26].
In healthcare, LLMs are evaluated on their ability to accurately process and analyze complex medical information. A systematic study focused on extracting patient data from unstructured medical reports found that GPT-4o achieved the highest overall accuracy at 91.4% across various categories like patient demographics and diagnostic details [27]. When benchmarked across multiple healthcare tasks, specialized medical LLMs typically provide more faithful answers than general models. However, general LLMs sometimes demonstrate better robustness and can generate more comprehensive (though potentially less accurate) content [28]. In a benchmark for global health, LLMs performed better than top human experts on questions related to tropical and infectious diseases, though their performance could be further improved with intentional fine-tuning using in-context learning [29].
After defining your use case and data type, the next step is to select a model from a platform like the Hugging Face Model Hub. This process involves several key technical decisions.
First, select a model family that aligns with your project’s technical requirements. Different open-source families are developed by various organizations and possess unique architectural strengths and performance characteristics.
You must also decide between a “chat” and an “instruct” model, a distinction based on the model’s fine-tuning and intended purpose.
While base models are trained to predict the next word in a sequence, these fine-tuned models are specialized for structured interactions. However, the distinction can sometimes be fluid, as it’s often possible to give instructions to chat models or have a conversation with an instruct model.
The size of a model, measured by its number of parameters (e.g., 8B for 8 billion), directly impacts its performance and computational cost. Larger models generally provide more accurate and nuanced outputs but require significantly more memory and processing power.
A practical rule of thumb is that you need approximately 2 bytes of VRAM per parameter to run a model in its standard 16-bit precision format (FP16 or BF16). For example, a 7-billion-parameter model requires about 14 GB of VRAM for inference. You can use tools like Hugging Face’s accelerate estimate-memory to calculate the precise memory requirements for a specific model without needing to download it first.
If your hardware resources are limited, you can use a quantized version of a model. Quantization is a compression technique that reduces a model’s memory footprint and accelerates inference by lowering the numerical precision of its weights—for example, from 16-bit floating-point numbers (FP16) down to 4-bit integers (INT4). This process involves a trade-off, as very low precision can sometimes slightly degrade model accuracy. Popular methods include GPTQ, AWQ, and Bitsandbytes.
Here is a real-world example of how to select an LLM for a customer support bot, following the step-by-step process of defining the use case, data type, and model specifications.
First, we define the specific application and its requirements.
Next, we identify the kind of data the model will need to process.
With the use case and data defined, we can now choose a specific model from the Hugging Face Hub by making several key technical decisions.
For this use case, the LLaMA 3 family is an excellent choice. It is a powerful, widely-supported open-source family known for its strong performance on a variety of benchmarks. Its use of Grouped-Query Attention (GQA) provides faster inference speed, which is critical for a responsive, real-time chatbot experience.
A chat model is the clear choice. These models are specifically fine-tuned on conversational data to handle multi-turn dialogues and maintain context, which is essential for a natural-feeling customer support interaction. An instruction-tuned model might handle a single command well but would struggle to manage the back-and-forth flow of a real conversation.
An 8-billion parameter model, such as Llama-3-8B-Instruct, offers the best balance of performance, speed, and cost for this application.
To deploy the chatbot in a cost-effective and scalable manner, we will use a quantized version of the model.
Llama-3-8B model reduces its memory requirement from ~16 GB to approximately 4-5 GB. This allows the chatbot to run on cheaper hardware, handle more concurrent users, and frees up VRAM for the RAG system’s retrieval components, leading to a more efficient and affordable production deployment.After selecting your model, the final steps are to verify its performance and deploy it. This process ensures the model meets your technical and quality standards before being integrated into a live application.
You should evaluate your chosen model’s capabilities using benchmarking, which involves testing it on standardized tasks and datasets to objectively measure its performance[43][44]. You can use established open benchmarks or create your own.
lighteval can help you build and run these tailored evaluations.Once you are satisfied with the model’s performance, you can deploy it. You can either use a managed cloud service or host it on your own hardware.
Choose an Inference Provider: For scalability and ease of management, you can use a managed inference provider. These platforms handle the complexities of GPU infrastructure and scaling[49]. When choosing a provider, key technical performance metrics to consider are latency (how quickly you get a response) and throughput (how many tokens per second the model can generate)[50].
Deploy Locally: If your priorities are privacy, cost control, or offline access, you can deploy the model on your own hardware. This gives you full control over your data and eliminates recurring API fees. A key consideration is hardware capacity; for example, a 70-billion parameter model like Llama-2-70B requires approximately 140 GB of VRAM to run in 16-bit precision, meaning it would need at least six 24GB GPUs[50]. Tools available to simplify local deployment include:
Here is a summary of the key stages:
By following a systematic process, you can successfully develop and launch a custom Large Language Model tailored to your specific needs. This structured approach guides you from the initial idea to a fully operational AI tool.