Ai tuning guide

Fine-Tuning Options for Major AI Models (2024)

Fine-tuning allows you to adapt pre-trained AI models to specific tasks (e.g., legal analysis, customer support, coding). Below is a comparison of fine-tuning capabilities across leading models:

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1. OpenAI (ChatGPT / GPT-4)

Fine-Tuning Options:

• GPT-3.5 Turbo (fine-tuning available)

• GPT-4 (no fine-tuning yet, only via prompt engineering)

• Custom fine-tuning via OpenAI API

How It Works:

1. Upload dataset (JSONL format with prompt-completion pairs).

2. Run fine-tuning job (OpenAI handles training).

3. Deploy custom model via API.

Pros & Cons:

✅ Easy to use (managed by OpenAI)
✅ Good for domain-specific chatbots (e.g., medical, legal)
❌ Expensive (~$0.008-$0.12 per 1K tokens)
❌ No fine-tuning for GPT-4 (only GPT-3.5)

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2. DeepSeek-V3

Fine-Tuning Options:

• Open-weight models available (check DeepSeek’s Hugging Face repo).

• Custom LoRA / full-parameter fine-tuning (self-hosted).

How It Works:

1. Download model weights (if open version available).

2. Fine-tune with PyTorch/Transformers (using LoRA or full training).

3. Deploy on own infrastructure (or cloud like AWS).

Pros & Cons:

✅ Free & open-weight option (unlike GPT-4)
✅ Strong coding/math capabilities (good for technical tasks)
❌ Requires ML expertise (not a managed service)

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3. Google Gemini 1.5

Fine-Tuning Options:

• Vertex AI (Gemini Pro fine-tuning in preview)

• Adapter-based tuning (Google’s proprietary method).

How It Works:

1. Upload dataset to Google Cloud.

2. Run tuning job via Vertex AI.

3. Deploy tuned model on GCP.

Pros & Cons:

✅ Integrates with Google Cloud (good for enterprises)
✅ Supports multimodal tuning (images + text)
❌ Limited availability (still in preview)
❌ Expensive compared to open models

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4. Claude 3 (Anthropic)

Fine-Tuning Options:

• No public fine-tuning yet (Anthropic focuses on RLHF alignment).

• Customization via "system prompts" (limited control).

Pros & Cons:

✅ Best safety controls (good for sensitive applications)
❌ No true fine-tuning (unlike OpenAI/Gemini)

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5. Meta (Llama 3)

Fine-Tuning Options:

• Full-parameter fine-tuning (8B, 70B versions).

• LoRA / QLoRA (efficient tuning) for smaller GPUs.

• Hugging Face integration (easy to customize).

How It Works:

1. Download Llama 3 weights (requires Meta approval).

2. Fine-tune with PyTorch + FSDP/RLHF.

3. Deploy via vLLM, TensorRT-LLM, etc.

Pros & Cons:

✅ Fully open-weight (best for research)
✅ Cost-effective (run on your own hardware)
❌ Weaker out-of-the-box than GPT-4
❌ Requires ML engineering skills

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6. Qwen (Alibaba)

**Fine-Tuning Options:

• Open-weight models (Qwen-72B, 1.8B, etc.)

• Supports LoRA, full fine-tuning

Pros & Cons:

✅ Strong Chinese/English bilingual support
✅ Apache 2.0 license (commercial use allowed)
❌ Less community support than Llama

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Comparison Table: Fine-Tuning Capabilities

Model	Fine-Tuning Available?	Method	Cost	Best For
GPT-4	❌ No (only GPT-3.5)	OpenAI API	$$$$	Enterprises needing quick tuning
DeepSeek-V3	✅ Yes (self-hosted)	LoRA / Full FT	Free-$	Developers wanting open models
Gemini 1.5	✅ (Vertex AI preview)	Adapter-based	$$$	Google Cloud users
Claude 3	❌ No (prompt-only)	N/A	-	Safety-critical apps
Llama 3	✅ Yes (full/LoRA)	Self-hosted	Free-$	Researchers, startups
Qwen	✅ Yes (open-weight)	LoRA / Full FT	Free-$	Chinese NLP tasks

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Which One Should You Choose?

• For ease of use: GPT-3.5 fine-tuning (OpenAI)

• For open-source flexibility: Llama 3 / DeepSeek

• For Google Cloud users: Gemini on Vertex AI

• For Chinese applications: Qwen

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