ankitkushwaha90/AI_agentic_pretrained_model_Identity_bin_ggug_safetensors

.bin vs .safetensors vs .ckpt vs .gguf: Which is Better?

Here is a clear, direct comparison to understand which is better and when to use each.

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1️⃣ What They Are

Format	Framework	Purpose
.bin	PyTorch	Stores model weights
.safetensors	PyTorch + others	Safer, faster loading weights
.ckpt	TensorFlow	Stores checkpoints/weights
.gguf	llama.cpp ecosystem	Quantized LLM weights for fast local inference
.onnx	Framework-agnostic	Interoperable optimized inference
.pt	PyTorch	Same as .bin technically

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2️⃣ Direct Comparison Table

Feature	.bin	.safetensors	.ckpt	.gguf
Framework	PyTorch	PyTorch, Transformers	TensorFlow	llama.cpp, KoboldCPP
Safety	❌ Can execute code if compromised	✅ Memory-safe, no code exec	❌ Can execute code	✅ Memory-safe
Speed	Standard	✅ Faster load times	Standard	✅ Fastest for quantized
Sharding	✅ Supported	✅ Supported	✅ Supported	❌ Always single-file
Quantization	❌ (external required)	❌ (external required)	❌ (external required)	✅ Integrated (Q2–Q8)
Disk usage	High	High	High	✅ Very low (quantized)
RAM usage	High	High	High	✅ Low
Inference Speed	Good	Good	Good	✅ Fastest locally
Cross-framework	❌ PyTorch only	❌ PyTorch only	❌ TF only	❌ llama.cpp only
Best for	General PyTorch LLM	Safer PyTorch LLM	TensorFlow LLM	Local CPU/GPU quantized inference

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3️⃣ When to Use Each

✅ .bin

• For PyTorch fine-tuning/training.
• Maximum compatibility with older scripts.
• Not recommended for sharing due to safety concerns.

✅ .safetensors

• Recommended for PyTorch + Transformers pre-trained models.
• Safer (no code execution risk).
• Often faster load, supports sharding for large models.
• Ideal for local workflows prioritizing safety and speed.

✅ .ckpt

• For TensorFlow workflows only.
• Standard for TensorFlow training and checkpoints.
• Not used in PyTorch/llama.cpp workflows.

✅ .gguf

• Best for local inference with quantized LLMs on llama.cpp, KoboldCPP, LM Studio, Ollama.
• Extremely low VRAM and RAM usage.
• Single-file, simple to manage.
• Inference-only (no fine-tuning/training).
• Ideal for chatbots, code generation, local CPU/GPU workflows.

✅ .onnx

• For framework-agnostic, hardware-accelerated inference.
• Useful for cross-platform model deployment.

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4️⃣ Which Is “Better”?

It depends on your use case:

✅ For local LLM inference (RTX 4060, CPU/GPU):

• Use .gguf quantized models.

✅ For PyTorch fine-tuning/training:

• Use .safetensors (preferred) or .bin if unavailable.

✅ For TensorFlow workflows:

• Use .ckpt.

✅ For cross-platform deployment:

• Use .onnx for hardware-optimized inference.

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✅ Recommendation for You (RTX 4060, 16GB RAM)

Use Case	Recommended
Run LLMs locally efficiently	🟩 .gguf
Fine-tune LLMs / Transformers	🟩 .safetensors
Serve LLM APIs on GPU	🟩 .safetensors
Use TensorFlow	🟩 .ckpt

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⚡ Additional Notes

• .gguf models have smallest disk/RAM/VRAM usage due to quantization.
• .safetensors is safest for PyTorch.
• Prefer .safetensors over .bin when downloading for security and speed.
• .gguf cannot be used for training; inference-only.
• .onnx is powerful for deployment flexibility.

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Need More?

If you want: ✅ A visual diagram summarizing this for your notes.
✅ A practical folder structure suggestion for your LLM workflow.
✅ Help with converting .bin ➔ .safetensors ➔ .gguf for local experiments.

Let me know!