LLM - Fine tune with custom data

Fine-tune OpenAI GPT models on your custom data, focusing on GPT-3.5 Turbo and experimental GPT-4, with tone, format, prompts, and json-ready outputs.

Prepare sample data for fine-tuning an OpenAI model by formatting conversations into messages with system, user, and assistant roles, using Colab, OpenAI APIs, and datasets.

Format the sample data for fine-tuning LLMs by converting conversations into OpenAI style messages, defining system, user, and assistant roles, and building a clean data set.

Explain a format error check on the data dictionary, then build a token counting function using tick token encoding to estimate tokens per message and per name and review distribution.

Review token counts and warnings using the OpenAI cookbook to ensure data quality. Chunk large data to fit 4096-token limits, estimate training cost, and prepare training and validation jsonl datasets.

Split your fine-tuning data into training and validation sets; feed training data to update model parameters while using validation data to assess generalization and detect overfitting.

Upload training and validation data to OpenAI; learn how training data updates model parameters and validation data provides a reliable assessment of model performance, preventing overfitting.

Learn to create and monitor a fine tune job using your training and validation data for GPT-3.5 turbo, including file IDs, suffixes, and hyperparameters, while tracking progress and events.

Learn to retrieve your fine tuned model's ID from a completed fine tune job and test it with a chat completion using a system message and your custom data.

Fine-tune pre-trained models on your own data with the gradient platform, using SDKs and CLI, for tasks like question answering, document summarization, sentiment analysis, and entity extraction.

Explore the gradient platform interface to create a workspace, fine tune a base model like llama 2 or bloom, and manage rag collections with q&a, summary, sentiment analysis, and personalization.

Learn to create a new gradient model with sample data using web UI or Python, select Hermes as the base model, prepare inputs-instructions-response data, then test with completion.

Tune the model with custom data by running three epochs using a sample dataset, applying the model adapter's fine tune method, then test with a qa completion.

Fine tune a GPT-3.5 turbo model using custom data by compiling Elon Musk tweets, formatting them into system and assistant messages in JSON, and deploying a Streamlit app.

Prepare a fine-tuning dataset in the required format, create and upload the training file, and launch a fine-tune job to build a custom model named Elon Twitter on GPT-3.5 turbo.

Fine-tune a custom data model and test it in OpenAI playground with GPT-3.5-turbo-0613 labeled Elon Twitter. Generate Elon Musk-style tweets on energy and environment and explore cost and data considerations.

Deploy a Streamlit app that compares a fine-tuned model against GPT-3.5 Turbo to generate Elon Musk–style tweets from a topic, with user feedback and a side-by-side view.

Discover how data extraction enables fine-tuning of language models by turning expense information into json format with fields like person name, amount, and item, using OpenAI prompts and sample data.

Explore how quantization reduces memory and computation for large language models, enabling efficient fine-tuning on your custom data to tailor tasks like text generation or sentiment analysis.

Explore quantization and custom precision, fp32, fp16, and 8-bit formats, to reduce memory and enable inference. Learn to fine-tune and deploy models via inference APIs and Huggingface, balancing accuracy.

Learn quantization basics, including symmetric and asymmetric methods, and how fp32/fp16 weights convert to binary with sign, exponent, mantissa; apply min-max scaling to map to int8 for custom data fine-tuning.

This lecture explains symmetric quantization using a minmax scaler to map 0–1000 to 0–255 for unsigned 8-bit numbers with the scale formula and rounding.

Master asymmetric quantization by using a scale and a zero point to map skewed ranges to unsigned integers, contrasting with symmetric quantization.

Apply post-training quantization by calibrating a pre-trained model to align predicted probabilities with actual frequencies. Use Platt scaling or isotonic regression to produce a calibrated, quantized model for new data.

Master quantization aware training to adapt pre-trained models for eight-bit inference by simulating quantization and calibration during training, preserving accuracy on memory-constrained devices.

Learn how to quantize large language models into the goof format for CPU and GPU inference, using four-bit quantization and Hugging Face snapshots.

Learn to convert a high memory language model to quantized fp16 goof format using blockwise quantization, apply quantization methods, and upload the quantized model to Hugging Face.

Explore the unsloth framework to fine-tune large language models with Onslaught, achieving up to 30x faster training and about 60% memory reduction.

Fine-tune the mistral 7b model for text generation on the IMDb dataset using transformers and TRL's sft trainer in Colab, deploying atop the onslaught fast language model.

Load IMDb movie data from Hugging Face datasets and use its text and label columns for sentiment analysis; split and load in Google Colab to fine-tune a model.

Set up the model tokenizer and peft model with four-bit quantization for mistral 7 million parameters, initialize the fast language model, and prepare LoRA parameters for fine-tuning.

Instantiate the supervised fine-tuned trainer (SFT trainer) with a Mistral base model, configure Hugging Face datasets and training arguments, and enable Lora fine-tuning with gradient checkpointing for custom data.

Test a fine-tuned 4-bit 7 billion parameters model with a tokenizer, generate text, decode outputs, and save the model to Google Drive, then upload to Huggingface for inference with onslaught.

Fine tune a large language model with your own Docker command data using Llama Factory, Gradio UI, and Hugging Face datasets for efficient customization.

Explore fine-tuning large language models with llama factory's gradio interface, selecting mistral seven b chats, applying lora adapters and sftt on your data from Huggingface, then chat with the adapter.

Tune large language models more cheaply and quickly by leveraging foundation LMs and small labeled data to tailor for specific tasks like text summarization and sentiment analysis.

Fine-tuned AI models pair your custom data with a foundation model to boost customer support and email handling. They enable translation into languages and generate tailored content as blog articles.

Avoid fine tuning when data is very small or changes frequently. Rely on simpler methods or available libraries in Python, REST, Java, or R to extract patterns from the data.