Apertus-70B-Instruct-2509-2048-Calibration-FP8

This is a premium FP8 quantized version of swiss-ai/Apertus-70B-Instruct-2509 featuring rigorous multi-dataset calibration for production-grade reliability.

Model Description

Property	Value
Base Model	Apertus-70B-Instruct-2509
Architecture	Dense (70B parameters)
Quantization	FP8 (E4M3 format) via llm-compressor
Target Hardware	NVIDIA Ada Lovelace & Hopper GPUs
Quantization Time	468.9 minutes (~7.8 hours)
Calibration Samples	2,048 (premium multi-dataset)

Usage

With Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "TevunahAi/Apertus-70B-Instruct-2509-2048-Calibration-FP8",
    torch_dtype=torch.float8_e4m3fn,
    device_map="auto",
    low_cpu_mem_usage=True,
)

tokenizer = AutoTokenizer.from_pretrained("TevunahAi/Apertus-70B-Instruct-2509-2048-Calibration-FP8")

# Generate
messages = [{"role": "user", "content": "Explain quantum computing"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

With vLLM (Recommended for production)

from vllm import LLM, SamplingParams

llm = LLM(model="TevunahAi/Apertus-70B-Instruct-2509-2048-Calibration-FP8")
sampling_params = SamplingParams(temperature=0.7, max_tokens=512)

prompts = ["Explain quantum computing"]
outputs = llm.generate(prompts, sampling_params)

Premium Calibration

This model was quantized using TevunahAi's premium multi-dataset calibration process:

Calibration Details

Total Samples: 2,048 (4-8x industry standard)
Datasets Used: 4 complementary sources
Coverage: Comprehensive across all use cases

Dataset	Samples	Purpose
Open-Platypus	512	STEM reasoning and logic
UltraChat-200k	512	Natural conversations
OpenHermes-2.5	512	Instruction following
SlimOrca	512	Diverse general tasks

Why Premium Calibration?

Most FP8 quantizations use 128-512 samples from a single dataset. TevunahAi uses 2,048 samples across 4 diverse datasets, ensuring:

✅ Superior robustness across task types
✅ Better statistical coverage for quantization scales
✅ Minimal quality loss compared to FP16
✅ Production-grade reliability
✅ Consistent performance on edge cases

When quality matters, choose TevunahAi 2048-Calibration FP8 quantizations.

Quantization Details

Target Layers: All Linear layers except lm_head
Precision: FP8 (E4M3 format)
Hardware Requirements: NVIDIA Ada Lovelace or Hopper (native FP8) or Ampere with emulation
VRAM Usage: ~70GB (fits on 2x RTX 4090 or 1x A100 80GB)

Quantization Infrastructure

Quantized on professional hardware optimized for high-quality model compression:

CPUs: Dual Intel Xeon Max 9480 (224 threads, 128GB HBM2e @ 2000 GB/s)
Memory: 256GB DDR5-4800 (16 DIMMs, 8-channel per socket, ~614 GB/s)
Total Memory Bandwidth: ~2,614 GB/s aggregate
GPU: NVIDIA RTX 5000 Ada Generation (32GB VRAM) with native FP8 support
Software: Ubuntu 25.10 | Python 3.12 | PyTorch 2.8 | CUDA 13 | llm-compressor

This infrastructure enables rigorous multi-dataset calibration that would be impractical on standard hardware.

Performance Notes

Quantization time: 468.9 minutes (~7.8 hours) with premium 2048-sample calibration
Memory during quantization: ~115GB (leveraging HBM2e + DDR5)
Memory reduction: ~~140GB FP16 → ~70GB FP8 (~~50% reduction)
Inference speed: 2-3x faster on Ada Lovelace GPUs vs FP16

About Apertus

Apertus-70B is a high-quality 70B parameter instruction-tuned model by Swiss AI, known for:

State-of-the-art reasoning capabilities
Strong multilingual support
Excellent instruction following
Apache 2.0 license

License

Apache 2.0 (same as original model)

Credits

Original model by Swiss AI
Quantized by TevunahAi
Quantization powered by llm-compressor

Why TevunahAi 2048-Calibration FP8?

The Difference is in the Details

Aspect	Standard FP8	TevunahAi 2048-Calibration FP8
Calibration Samples	128-512	2,048
Datasets	Single	4 diverse
Edge Case Handling	Adequate	Superior
Output Consistency	Good	Excellent
Production Ready	Maybe	Absolutely