Self-hosted LLMs (Llama, Mistral) in Prompt Engineering / GenAI - Model Pipeline Trace

Model Pipeline - Self-hosted LLMs (Llama, Mistral)

This pipeline shows how self-hosted large language models (LLMs) like Llama and Mistral process text data. It covers loading data, preparing it, running the model to learn patterns, improving through training, and finally generating text predictions.

Data Flow - 6 Stages

1Data in

1000 text samples→Raw text data collected from various sources→1000 text samples

"Hello, how are you?", "What is AI?", "Tell me a story."

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2Preprocessing

1000 text samples→Tokenization and cleaning (lowercase, remove punctuation)→1000 sequences of tokens (variable length)

"hello how are you", "what is ai", "tell me a story"

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3Feature Engineering

1000 sequences of tokens→Convert tokens to numerical IDs and pad sequences→1000 sequences x 128 tokens (padded)

[101, 7592, 2129, 2024, 2017, 102, 0, 0, ...]

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4Model Trains

1000 sequences x 128 tokens→Feed sequences into LLM transformer layers to learn patterns→1000 sequences x 128 tokens x 32000 vocab logits

Logits represent scores for each word in vocabulary at each token position

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5Metrics Improve

Training outputs→Calculate loss and accuracy to improve model weights→Loss decreases, accuracy increases over epochs

Epoch 1 loss=3.2, accuracy=0.25; Epoch 5 loss=1.1, accuracy=0.65

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6Prediction

New input text tokens→Model generates next word probabilities and outputs text→Generated text sequence

Input: "What is AI?" Output: "AI is the simulation of human intelligence by machines."

Training Trace - Epoch by Epoch

3.2 |*       
2.5 | **     
1.8 |  ***   
1.3 |   **** 
1.1 |    *****
    +---------
     1 2 3 4 5
     Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	3.2	0.25	Model starts learning basic language patterns
2	2.5	0.40	Loss decreases, accuracy improves as model learns
3	1.8	0.52	Model captures more complex language features
4	1.3	0.60	Training converges, model predictions get better
5	1.1	0.65	Model ready for generating coherent text

Prediction Trace - 5 Layers

Layer 1: Input Tokenization

Layer 2: Embedding Layer

Layer 3: Transformer Layers

Layer 4: Output Layer (Softmax)

Layer 5: Text Generation

Model Quiz - 3 Questions

What happens to the loss value as the model trains over epochs?

A It increases steadily

B It decreases steadily

C It stays the same

D It randomly jumps up and down

Key Insight

Self-hosted LLMs like Llama and Mistral transform raw text into numbers, learn patterns through layers, and improve by reducing loss. This process enables them to generate meaningful text predictions based on learned language understanding.