Mobius-LLM-Fine-tuning-Engine

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Mobius-LLM-Fine-tuning-Engine

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LLM Fine-tuning Engine with GUI and ML Core

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# Mobius — LLM Fine-tuning Engine

Mobius is an AI engineering toolkit for fine-tuning, evaluating and deploying large language models (LLMs). This repository focuses on reproducible model development: data pipelines, fine-tuning strategies, prompt engineering, evaluation harnesses, and deployment patterns that move work from experimentation to production.

This README expands and clarifies the project's AI/LLM design, model-choice reasoning, prompt-engineering and evaluation logic, and the higher-level architecture decisions that make Mobius an AI engineering project — not just an API wrapper.

---

## Table of contents

- Project goals
- What “AI engineering” means for Mobius
- High-level architecture
- Explicit AI / LLM components
- Model choice: reasoning and trade-offs
- Fine-tuning strategies supported
- Prompt engineering, testing and evaluation logic
- AI system design decisions (safety, reproducibility, observability)
- Quickstart (recommended workflow)
- Example configuration and prompt templates
- Contributing and license

---

## Project goals

- Provide a lightweight, reproducible engine for fine-tuning and evaluating LLMs.
- Treat fine-tuning as an engineering practice: clear inputs, testable outputs, CI-friendly evaluations, observability, and safe defaults.
- Support modern fine-tuning approaches (parameter-efficient fine-tuning, instruction tuning, RLHF-ready hooks).
- Make model, data, prompt, and metric choices explicit and auditable.

---

## What “AI engineering” means for Mobius

AI engineering here emphasizes:
- Explicitness: every experiment has a versioned config (model, tokenizer, dataset, hyperparameters, prompt templates, metrics).
- Repeatability: pipelines should be runnable end-to-end with deterministic seeds and checkpoints.
- Observability: training metrics, evaluation suites, and inference telemetry are first-class.
- Safety & governance: data lineage, filtering, and policy checks before model promotion.
- Deployment-readiness: artifacts include model, tokenizer, deployment config, and evaluation reports.

---

## High-level architecture

- Data ingestion & preprocessing: dataset loaders, quality checks, deduplication, and augmentation.
- Tokenization & encoding: tokenizer selection and tokenizer-training hooks for domain-specific vocabularies.
- Model selection & adapter layer: base model + optional adapter (LoRA/PEFT) or full fine-tune.
- Trainer & scheduler: training loop with mixed precision, gradient accumulation, checkpointing, and evaluation callbacks.
- Prompt / policy layer: templates, instruction wrappers, and inference-time wrappers (safety, rate control).
- Evaluation harness: automatic metrics, unit tests, human annotation integration, and test-suite versioning.
- Serving & monitoring: lightweight API, versioning, A/B routing, latency and correctness telemetry.

---

## Explicit AI / LLM components

This section lists the conceptual components Mobius treats as first-class.

- Dataset
  - Source (files, DBs, scraped content)
  - Schema (instruction-response, chat history, classification pairs)
  - Validation (schema checks, profanity / PII filters)
  - Splits & sampling strategies (train/val/test, stratified sampling)
- Tokenizer
  - Choice (Byte-Pair Encoding, SentencePiece, Unigram)
  - Tokenizer training hooks for domain Vocab
  - Tokenization checks (max length, truncation strategy)
- Model (Base / Candidate Models)
  - Base model family and size (e.g., LLaMA / Mistral / Falcon / GPT-family)
  - Adapter pattern support (LoRA, adapters, prefix tuning)
  - Checkpoint management and conversion utilities
- Fine-tuning Engine
  - Training loop with config-driven hyperparameters
  - PEFT hooks (LoRA/Adapter)
  - Mixed precision training (fp16/bf16)
  - Checkpoint, resume, and experiment metadata recording
- Prompt Layer
  - Template management (named templates, variables, few-shot examples)
  - Prompt normalization & canonicalization
  - Safety wrappers and filters at inference time
- Evaluation Suite
  - Automatic metrics (perplexity, BLEU/ROUGE, exact match, accuracy)
  - Task-specific scorers (F1, AP, MRR)
  - Human-in-loop evaluation workflows and annotation schema
  - Regression and acceptance tests (min-performance thresholds)
- Serving & Ops
  - Model packaging (HF format / TorchScript / ONNX when applicable)
  - API adapters for synchronous and streaming responses
  - Observability (latency, error rates, model drift signals)

---

## Model choice: reasoning and trade-offs

Selecting a model for fine-tuning or deployment is a multi-dimensional trade-off. Consider the following axes:

- Capability vs cost
  - Larger models generally achieve stronger capabilities but require more compute for fine-tuning and inference.
  - For many tasks, smaller models with instruction-tuning or adapters can hit acceptable performance at far lower cost.
- Latency and throughput
  - Production services often favor models that meet latency targets under resource constraints.
  - Choose smaller or quantized models if strict latency SLAs are required.
- Data efficiency
  - If you have limited labeled data, parameter-efficient fine-tuning (LoRA, adapters) and instruction tuning from a high-quality seed model are preferred.
- Licensing and ecosystem
  - Open-source models (e.g., LLaMA-compatible checkpoints, Mistral, Falcon) vs commercially-hosted APIs — evaluate license terms, usage limits, and export compliance needs.
- Safety and alignment
  - Models with alignment training or content filters can reduce downstream moderation burden; however, verify biases and failure modes with targeted tests.

Recommended pragmatic approach:
- Start with a mid-sized open model (cost/accuracy sweet spot).
- Use PEFT (e.g., LoRA) for fast iteration.
- Evaluate with a representative validation suite (see evaluation section).
- If needed, scale up to larger model families only after verifying dataset quality and prompt design.

---

## Fine-tuning strategies supported (and why you’d pick them)

- Full fine-tuning
  - Pros: full model capacity available
  - Cons: expensive, harder to maintain multiple fine-tuned variants
  - Use when you need maximal performance and have compute & data
- Parameter-Efficient Fine-Tuning (PEFT: LoRA, Adapters, Prefix Tuning)
  - Pros: fast, smaller checkpoints, cheaper to store & serve
  - Cons: sometimes slightly lower peak accuracy but often comparable for many tasks
  - Recommended first approach for most use cases
- Instruction Tuning
  - Fine-tune for instruction-following behaviors using curated instruction-response pairs.
  - Good for building assistant-like behaviors.
- RLHF (REINFORCE / PPO hooks)
  - For alignment-sensitive behaviors and preference optimization when you have reward models and annotation budgets.

---

## Prompt engineering and evaluation logic

Prompt engineering is part of the model's interface and must be versioned and tested.

- Prompt templates
  - Keep named, parameterized templates in the repo (e.g., "classification_v1", "summarize_with_constraints").
  - Template example:
    - Instruction: "Summarize the following for a 5th-grade audience. Output must be <= 120 words."
    - Context: {context}
  - Use examples for few-shot prompts stored alongside templates and idempotently referenced by experiments.

- Prompt testing
  - Unit tests for templates (ensures placeholder substitution and edge-case inputs)
  - Sanity checks: length, tokens, and presence of required fields

- Evaluation harness
  - Automatic metrics:
    - Language modeling: perplexity on held-out data
    - Generation: ROUGE, BLEU where appropriate
    - Task metrics: accuracy, F1, MRR depending on task
  - Behavioral tests:
    - Safety tests (toxic content, disallowed instructions)
    - Consistency tests (same prompt stability under minor edits)
  - Human evaluation:
    - Define annotation schema (clarity, factuality, helpfulness)
    - Use randomized A/B evaluations with inter-annotator agreement monitoring
  - Regression criteria:
    - Define promotion gates (e.g., new model must have ≤5% drop in task accuracy and no new safety regressions)

- Prompt optimization loop
  1. Design template variants and candidate few-shot examples.
  2. Run batch inference on a validation suite.
  3. Aggregate metrics, run safety checks, and produce a ranked list of variants.
  4. Optionally incorporate human feedback into a reward model for RLHF/Preference tuning.

---

## AI system design decisions (safety, reproducibility, observability)

- Reproducibility
  - All experiments must be defined by a single config file (model, tokenizer, dataset, seed, hyperparameters, prompt id).
  - Save deterministic seeds and environment metadata.
- Data governance
  - Maintain provenance for each dataset: source URL, filtering steps, and accepted license.
  - PII detection and scrub pipelines are enforced before training.
- Observability & Monitoring
  - Log training and evaluation metrics to a metrics backend (Prometheus/MLFlow).
  - Inference telemetry: latency, tokens-per-request, error types, and drift signals (distributional checks).
- Safety & Moderation
  - Automated safety filter step before model promotion: toxicity, instruction-following checks, jailbreak regressions.
- Versioning & Promotion
  - Model artifacts, tokenizer, prompt templates and evaluation reports are bundled into a "release" artifact for promotion to staging/production.
- Cost controls
  - Use adapter-based workflows for experimentation; reserve full fine-tuning for release candidates that pass validation.

---

## Quickstart (recommended workflow)

1. Prepare dataset and config (example: config/train_config.yaml).
2. Choose a base model and fine-tune strategy (recommended: PEFT/LoRA for fast iteration).
3. Run preprocessing:
   - python scripts/preprocess.py --config config/train_config.yaml
4. Train:
   - python scripts/train.py --config config/train_config.yaml
5. Evaluate:
   - python scripts/eval.py --config config/eval_config.yaml
6. Package model with tokenizer and evaluation report into a release bundle:
   - python scripts/package_release.py --config config/release.yaml

(Adapt the command names to your local tooling — the above illustrates the flow.)

---

## Example prompt template (canonicalized)

Prompt name: summarize_with_constraints_v1

Template:
"Instruction: Summarize the text below for a general audience. Keep summary under {max_tokens} tokens and avoid speculation. Text:\n\n{context}\n\nSummary:"

Usage:
- Fill {context}, set {max_tokens} (e.g., 120), and call the inference wrapper that enforces safety filters and truncation.

---

## Example evaluation logic (pseudocode)

- Run batched inference on validation set using selected template
- Compute:
  - Perplexity (if model log-probabilities available)
  - ROUGE-L for summarization tasks
  - Task-specific accuracy / F1
- Run safety suite (toxicity check, prohibited content triggers)
- Produce a JSON report:
  - metrics: {accuracy: x, rouge_l: y, perplexity: z}
  - safety: {toxicity_rate: t, blocked: k}
  - human_eval: {if available}

Use this report to decide promotion; set threshold gates in CI to prevent accidental promotion.

---

## Framing this as AI engineering

Mobius is meant to be consumed as an engineering workflow:
- Version everything (data, prompts, configs).
- Automate evaluations and gate model promotion.
- Favor PEFT for experimentation and use full fine-tuning selectively.
- Treat prompts as code: test them, version them, and include them in releases.

---

## Contributing

Contributions are welcome. Key areas:
- New model family adapters and conversion utilities
- Additional evaluation metrics and test suites
- More CI/automatic promotion rules and examples
- Human-in-the-loop annotation integrations

Please open issues or PRs and follow the code of conduct.

---

## License

Apache 2.0

---

## Further reading and references

- PEFT / LoRA patterns: [PEFT on Hugging Face](https://huggingface.co/docs/peft/)
- Prompt engineering concepts and best practices
- Evaluation frameworks and human evaluation design

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