JobCannon
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LLM Fine-Tuning

Customizing large language models for domain-specific applications

тмв рд╢реНрд░реЗрдгреА 3рддрд╛рдВрддреНрд░рд┐рдХ
+$40k-
рдкрдЧрд╛рд░рд╛рд╡рд░реАрд▓ рдкрд░рд┐рдгрд╛рдо
8 рдорд╣рд┐рдиреЗ
рд╢рд┐рдХрдгреНрдпрд╛рд╕ рд▓рд╛рдЧрдгрд╛рд░рд╛ рд╡реЗрд│
рдХрдареАрдг
рдХрд╛рдард┐рдгреНрдп
12
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рдПрдХрд╛ рджреГрд╖реНрдЯрд┐рдХреНрд╖реЗрдкрд╛рдд

LLM fine-tuning adapts foundation models (GPT, Llama, Mistral, Claude) to domain-specific tasks with parameter-efficient methods (LoRA/QLoRA) or full training. Career path: Practitioner (OpenAI API, basic data prep, $140-170k) тЖТ Specialist (LoRA/RLHF, Hugging Face ecosystem, $160-210k) тЖТ Expert (distributed training, custom objectives, $200-280k). Salaries top-tier: USA $130-280k, UK ┬г75-160k, EU тВм85-175k. Techniques: LoRA (low-rank adaptation, 10% compute of full tune), QLoRA (quantized, fit on single GPU), RLHF/DPO (alignment), evaluation frameworks. When to fine-tune: if domain-specific performance >>general model, or budget allows; otherwise RAG or prompt engineering may suffice.

LLM Fine-Tuning рдореНрд╣рдгрдЬреЗ рдХрд╛рдп

LLM fine-tuning adapts pre-trained language models to specific domains, tasks, or styles. Techniques range from full fine-tuning to parameter-efficient methods (LoRA, QLoRA) that require minimal compute. Fine-tuning enables creating specialized models that outperform general-purpose LLMs for specific use cases. Understanding when to fine-tune vs use prompt engineering or RAG, and how to prepare training data, is a critical skill for AI engineers building production AI applications.

ЁЯФз рд╕рд╛рдзрдиреЗ рдЖрдгрд┐ рдкрд░рд┐рд╕рдВрд╕реНрдерд╛
HuggingFace TransformersPEFTTRLAxolotlUnslothMLflowWeights & BiasesModalReplicateOpenAI fine-tuning API

ЁЯУЛ рд╕реБрд░реВ рдХрд░рдгреНрдпрд╛рдкреВрд░реНрд╡реА

Machine Learning AiPrompt Engineering AdvancedNatural Language ProcessingPython

ЁЯТ░ рдкреНрд░рджреЗрд╢рд╛рдиреБрд╕рд╛рд░ рдкрдЧрд╛рд░

рдкреНрд░рджреЗрд╢рдЬреНрдпреБрдирд┐рдпрд░рдордзреНрдпрдорд╕реАрдирд┐рдпрд░
USA$140k$210k$280k
UK┬г75k┬г118k┬г160k
EUтВм85kтВм130kтВм175k
CANADAC$145kC$218kC$290k

тЪЦ рдпрд╛рдВрдЪреНрдпрд╛рд╢реА рддреБрд▓рдирд╛ рдХрд░рд╛

Prompt Engineering AdvancedRag ArchitectureAi Agent DevelopmentMlopsModel Deployment

тЭУ FAQ

Full fine-tune vs LoRA, which should I use?
LoRA: 1-2% of parameters, 90% faster, fits single GPU, 90% of quality gain. Full fine-tune: all parameters, slower, needs multi-GPU, slightly higher quality, overfitting risk on small datasets. Start with LoRA for exploration; full fine-tune only if you have >100k labeled examples and compute budget.
When should I fine-tune vs use RAG vs better prompting?
Prompting: < 1 week, free, baseline. RAG: 1-2 weeks, retrieval-based, no training data needed, good for knowledge updates. Fine-tuning: 2-8 weeks, requires labeled data, permanent model changes, best when general model fundamentally misses your domain (medical, legal, code).
How much training data do I need for LoRA?
LoRA: 500-5000 examples (domain-specific task). Full fine-tune: 5000-100k+. Quality > quantity: 500 excellent examples beat 10k mediocre ones. BLEU, ROUGE, or task-specific metrics guide sufficiency. Start with 10% of your budget to validate data quality.
What's RLHF and DPO, and when do I use them?
RLHF (Reinforcement Learning from Human Feedback): align model to human preferences via reward model, computationally expensive, industry standard (ChatGPT). DPO (Direct Preference Optimization): simpler, no reward model, same alignment quality, emerging 2024-2026. Use DPO for most cases unless you have production RLHF infrastructure.
How do I evaluate a fine-tuned model?
Automated: BLEU/ROUGE (translation/summarization), METEOR, exact match (QA). Human: n=100 blind test vs baseline. Business: latency, cost, throughput. Always test on held-out set (10-20% of data) to catch overfitting. Compare model outputs qualitatively.
What are GPU cost implications for fine-tuning?
LoRA on single GPU (H100): $2-8/hour. Full fine-tune on multi-GPU: $50-300+/hour. 1-week project: $2k-10k total. OpenAI API (per 1M tokens): $1.50-$30 depending on model. Calculate ROI: if fine-tuned model saves 10% on inference costs or increases quality by 20%, payoff within months.
Which base model should I fine-tune, 7B, 13B, 70B, or use an API?
7B (Mistral, Llama-2): fastest, cheapest, edge deployment, good for latency-critical tasks. 13B-70B: better quality, slower. 70B: near-GPT4 but expensive to run. API fine-tuning (OpenAI, Anthropic): zero ops, blackbox, most expensive. For startups: start 7B LoRA locally, graduate to 13B, switch to API only if custom tuning impossible.

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рдХрд░рд┐рдЕрд░ рдореЕрдЪ рдХрд░реВрди рдкрд╛рд╣рд╛ тАФ рдЖрдореНрд╣реА рдпреЛрдЧреНрдп рдорд╛рд░реНрдЧ рд╕реБрдЪрд╡реВ.

рдорд╛рдЭреНрдпрд╛рд╕рд╛рдареА рд╕рд░реНрд╡реЛрддреНрддрдо рдХреМрд╢рд▓реНрдпреЗ рд╢реЛрдзрд╛ тЖТ

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рдХрд░рд┐рдЕрд░ рдореЕрдЪ рдХрд░реВрди рдкрд╛рд╣рд╛ тАФ рдореЛрдлрдд тЖТ