AI and natural language processing

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1. Explore AI model development and generative intelligence

   Teknosys AI Research & Development Welcome to Teknosys, a hands-on space for exploring AI model development, large language models, and different text-generatio

2. AI model development

   Explore the model-development lifecycle: preparing datasets, selecting a base model, understanding training and fine-tuning, evaluating outputs, and integrating

3. Text-generation techniques

   Experiment with prompts for question answering, summarization, rewriting, creative writing, structured responses, and code generation. Learn how temperature, to

4. GPT-style language models

   Understand generative pretrained transformers: models that predict successive tokens to produce text from a prompt. Explore tokenization, attention, conversatio

5. Hugging Face model workflows

   Discover the Hugging Face ecosystem for finding pretrained models, tokenizers, datasets, and model cards. Explore workflows for loading compatible models, gener

6. GGUF and efficient local inference

   Learn how compatible models can be converted and quantized into GGUF, a model-file format used by inference tools such as llama.cpp and supported model deployme

7. Try the Teknosys-built AI experience

   Use the AI chat to explore a topic, generate a draft, analyze text, or ask for code with an explanation. The Teknosys-built chat interface connects to models ho

8. Natural language processing: from understanding to generation

   Natural language processing (NLP) brings together machine learning and linguistics to work with human language. It covers tasks such as text classification, sen

9. BERT: bidirectional language understanding

   BERT (Bidirectional Encoder Representations from Transformers) is an encoder-based model that uses context from both sides of a token. Its masked-language-model

10. GPT: autoregressive text generation

    GPT-style models use a decoder-based transformer with causal attention: each new token is predicted from the preceding context. Pretraining learns language patt

11. RNN, LSTM, and GRU: learning sequences

    Recurrent neural networks (RNNs) process sequences step by step, carrying a hidden state between tokens. Long short-term memory (LSTM) networks and gated recurr

12. Recursive networks and RNTN

    Recursive neural networks combine representations along a structure such as a sentence parse tree, rather than simply following token order. Recursive neural te

13. Encoder-decoder models: transforming text

    Models such as T5 and BART combine an encoder that reads input with a decoder that generates output. This sequence-to-sequence setup is well suited to translati

14. Embeddings, retrieval, and responsible evaluation

    Embeddings map text into numeric vectors so related meanings can be compared. Retrieval systems can use these vectors to find relevant passages, and RAG can sup

15. Build your own language model: a practical learning path

    Start with a small, measurable task and an existing checkpoint before attempting expensive pretraining from scratch. Read each article for concepts, examples, a

16. 1. Define the task and choose a development route

    Decide whether you need a classifier, a summarizer, a conversational assistant, or a model for another task. Define the languages, latency, hardware budget, acc

17. 2. How to supply a dataset, not just “inject” it

    Training data is passed through a tokenizer, batched into tensors, and used to calculate a loss. Backpropagation and an optimizer then update weights. Pasting a

18. 3. What is Ollama?

    Ollama is a local model runtime and management tool. It can download supported models, run generation, and expose HTTP endpoints such as and . It is not a repla

19. 4. What is Hugging Face Hub?

    The Hub hosts versioned repositories for models, datasets, and application demos called Spaces. A model repository can contain weights, configuration, tokenizer

20. 5. How neural networks are developed

    Choose layers and a loss function, initialize parameters or load a pretrained checkpoint, and run a forward pass to produce predictions. Compare predictions wit

21. 6. BERT, recurrent networks, and RNTN

    BERT is a bidirectional transformer encoder, commonly adapted for classification, token labeling, and extractive question answering. RNNs process sequences with

22. 7. Transformer NLP: tokens, attention, and generation

    A tokenizer splits text into token IDs. Embeddings and position information represent them; attention combines relevant context, and feed-forward layers refine

23. 8. State tracking, backtracking, and backpropagation

    State tracking records relevant conversation turns, task progress, and tool results. A transformer's key/value cache accelerates inference for preceding tokens;

24. 9. A numerical weight update and a backtracking example

    Consider one weight with prediction and squared loss . With input 2, target 4, weight 1, and learning rate 0.1: This is a toy gradient-descent step, not a reali

25. 10. GPT-2, T5, BART, and PEGASUS compared

    GPT-2: a decoder-only, next-token language model for text continuation. Its original checkpoints are not instruction-tuned assistants. T5: an encoder-decoder th

26. 11. Measure generated-text quality: BLEU and ROUGE

    BLEU measures modified n-gram precision against reference texts with a brevity penalty; it is traditionally used for translation. ROUGE —not “ROGUE”—is a family

27. 12. Evaluate PEGASUS on CNN/DailyMail

    CNN/DailyMail pairs news inputs with reference summaries. This smoke test evaluates ten held-out examples using . Ten examples are not a benchmark; expand evalu

28. 13. Fine-tune PEGASUS with paired summaries

    Continue in the Python environment above, reusing its model and tokenizer. This small training run starts from an already CNN/DailyMail-adapted checkpoint; it d

29. 14. Build and publish a language-model workflow with the Hub

    Select a supported checkpoint, load its matching tokenizer, prepare task-appropriate datasets, train locally, and save model/tokenizer artifacts. Test inference

30. 15. What is an “uncensored” model?

    “Uncensored” is an informal label, often describing models with fewer refusal behaviors or different alignment data. It does not guarantee neutrality, accuracy,

31. 16. Put guardrails around every model release

    Before training: verify data permissions, remove secrets, document provenance, and define acceptable-use policies. Before release: test harmful-request handling

32. 17. Can AI crack Bitcoin private keys?

    There is no known practical method using today's AI or conventional computers to recover a securely generated Bitcoin private key from its public key or address

33. 18. Generative AI: capabilities and limits

    Generative AI learns patterns that can produce new text, images, audio, or code conditioned on input. Language models generate tokens; other systems may use dif

34. Fine-tuning example with Hugging Face TRL

    Supervised fine-tuning (SFT) adapts a language model using examples from a training dataset. This Python example uses TRL's with an already loaded and a contain

35. Create a GGUF model on Fedora

    This local-terminal workflow builds llama.cpp, downloads a compatible Hugging Face model, converts its weights to GGUF, and creates a quantized version for infe

36. Install system dependencies

    Install the compiler, build tools, and Python prerequisites. Installing explicit packages avoids differences in development-group names across Fedora versions.

37. Clone and build llama.cpp

    For NVIDIA GPU support, add to the configuration command only after installing a compatible CUDA toolkit and driver. That option enables the CUDA backend for in

38. Set up the Python conversion environment

    Continue from the llama.cpp directory and use a virtual environment.

39. Download a compatible Hugging Face model

    Replace with the repository you intend to convert. For gated models, accept the required terms and authenticate with . Keep access tokens out of shared commands

40. Convert the model to a high-precision GGUF file

    The converter reads the model configuration, tokenizer, and weights. This example writes an FP16 GGUF file. Supported architectures and output types depend on t

41. Quantize and try the model

    is a common starting point for balancing size and quality, not a universal optimum. Compare quantization levels against your hardware and tasks, and keep the hi

42. Start experimenting with AI chat

    Open the interactive AI chat to explore these topics. Try a prompt such as “Explain how GPT-style models generate text,” “Compare top-k and top-p sampling,” or

43. 3. Transformer Anatomy: a guided chapter

    Chapter 3 · Transformer Anatomy A transformer is not a single model or a synonym for a chatbot. It is a family of neural-network designs that turn sequences of

44. Transformer architecture: from tokens to predictions

    Transformer architecture is easiest to understand as a data pipeline with two operations repeated many times: tokens exchange information through attention, the

45. The transformer encoder: building contextual representations

    An encoder converts a visible input sequence into contextual token representations. Unlike a simple embedding lookup, its output for “charge” can change dependi

46. Self-attention: queries, keys, values, and masks

    Self-attention lets each token construct an input-dependent mixture of information from other tokens in the same sequence. “Self” describes the shared source of

47. The feed-forward layer: transforming each token

    Attention moves information between positions. A position-wise feed-forward network changes how the features at each position are represented. Both are needed:

48. Layer normalization and residual connections

    Deep transformer stacks repeatedly alter the scale and content of hidden states. Layer normalization and residual connections address different aspects of this

49. Positional embeddings: giving tokens an order

    “The customer refunded the shop” and “The shop refunded the customer” contain the same words but describe different events. A transformer needs access to orderi

50. Adding a classification head to an encoder

    A pretrained encoder produces hidden states, not the business labels your application needs. A classification head connects those representations to a task-spec

51. The transformer decoder: generating one token at a time

    A transformer decoder predicts an output sequence from an available prefix. In an encoder-decoder translator it also reads encoded source information. In a typi

52. Meet the transformers: reading model specifications

    Meeting a transformer model should begin with its specification, not a demo answer. Two checkpoints can both be called language models while expecting different

53. The transformer tree of life: three architectural branches

    The “transformer tree of life” is a useful learning map, not a literal ancestry diagram. Its three familiar branches are encoder-only, decoder-only, and encoder

54. The encoder branch: BERT, RoBERTa, and representation learning

    The encoder branch focuses on turning supplied inputs into representations that downstream tasks can use. Its common bidirectional attention pattern makes both

55. The decoder branch: GPT-style language models

    The decoder branch models a sequence as a series of next-token predictions. Its causal attention means a position cannot use later target tokens to solve its pr

56. The encoder-decoder branch: T5, BART, and sequence transformation

    The encoder-decoder branch keeps source processing and target generation as connected but distinct stages. The encoder reads the supplied input with its permitt