NextCoder-32B-2048-Calibration-FP8

This is a premium FP8 quantized version of microsoft/NextCoder-32B featuring rigorous code-optimized multi-dataset calibration for production-grade reliability.

Model Description

Property	Value
Base Model	NextCoder-32B
Architecture	Dense (32B parameters)
Quantization	FP8 (E4M3 format) via llm-compressor
Target Hardware	NVIDIA Ada Lovelace & Hopper GPUs
Quantization Date	2025-11-27
Quantization Time	194.0 minutes (~3.2 hours)
Calibration Samples	2,048 (premium code-optimized)

Usage

With Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "TevunahAi/NextCoder-32B-2048-Calibration-FP8",
    torch_dtype=torch.float8_e4m3fn,
    device_map="auto",
    low_cpu_mem_usage=True,
)

tokenizer = AutoTokenizer.from_pretrained("TevunahAi/NextCoder-32B-2048-Calibration-FP8")

# Generate
messages = [{"role": "user", "content": "Write a Python function to calculate fibonacci numbers:"}]
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/NextCoder-32B-2048-Calibration-FP8")
sampling_params = SamplingParams(temperature=0.7, max_tokens=512)

prompts = ["Write a Python function to calculate fibonacci numbers:"]
outputs = llm.generate(prompts, sampling_params)

Premium Code-Optimized Calibration

This model was quantized using TevunahAi's premium code-focused calibration process:

Calibration Details

Total Samples: 2,048 (4-8x industry standard)
Datasets Used: 4 code-focused sources
Coverage: Comprehensive across coding tasks

Dataset	Samples	Purpose
HuggingFaceH4/CodeAlpaca_20K	512	Code instruction pairs
garage-bAInd/Open-Platypus	512	STEM/reasoning (includes code)
teknium/OpenHermes-2.5	512	Diverse instructions
theblackcat102/evol-codealpaca-v1	512	Evolved code examples

Why Code-Optimized Calibration?

Most FP8 quantizations use generic chat data for calibration. TevunahAi uses 2,048 samples from 4 code-focused datasets, ensuring:

✅ Superior code generation quality
✅ Better handling of programming syntax
✅ Optimized for multiple languages
✅ Accurate completion of complex code
✅ Production-grade reliability for coding tasks

For code models, generic calibration isn't enough. TevunahAi uses code-specific data.

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: ~32GB (fits on RTX 4090, A100, or 2x RTX 4080)

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
Peak Memory Usage: ~319GB during quantization
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 impossible on standard hardware.

Performance Notes

Quantization time: 194.0 minutes with premium 2048-sample calibration
Memory during quantization: ~319GB (model + calibration datasets)
Memory reduction: ~~64GB FP16 → ~32GB FP8 (~~50% reduction)
Inference speed: 2-3x faster on Ada Lovelace GPUs vs FP16

About NextCoder

NextCoder-32B is Microsoft's flagship next-generation code model, featuring:

State-of-the-art code generation capabilities
Strong performance across multiple programming languages
Excellent instruction following for coding tasks
Largest model in the NextCoder family
MIT license

NextCoder Family Comparison

TevunahAi provides premium FP8 quantizations for the entire NextCoder family:

Model	Parameters	Quantization Time	VRAM Usage
NextCoder-7B-2048-Calibration-FP8	7B	50.9 min	~7GB
NextCoder-14B-2048-Calibration-FP8	14B	91.3 min	~14GB
NextCoder-32B-2048-Calibration-FP8 (this)	32B	194.0 min	~32GB

All models calibrated with identical premium 2048-sample code-focused datasets.

Comparison: Standard vs Premium Calibration

TevunahAi offers two quantization tiers for this model:

Version	Calibration	Samples	Datasets	Use Case
Standard FP8	Basic	256	1	Quick deployment
Premium FP8 (this)	Code-optimized	2,048	4 code-focused	Production-grade

When to Choose Premium:

✅ Production deployments
✅ Quality-critical applications
✅ API services at scale
✅ Benchmarking and evaluation

When Standard is Fine:

✅ Quick testing
✅ Development/prototyping
✅ Resource-constrained environments
✅ Non-critical applications

License

MIT (same as original model)

Credits

Original model by Microsoft
Quantized by TevunahAi
Quantization powered by llm-compressor

Why TevunahAi 2048-Calibration FP8?

Task-Optimized Calibration

TevunahAi doesn't use one-size-fits-all calibration:

Model Type	Calibration Focus
Code Models	Code-specific datasets (CodeAlpaca, evol-codealpaca)
General Models	Diverse instruction datasets (UltraChat, SlimOrca)

The right calibration for the right model.

The Difference is in the Details

Aspect	Standard FP8	TevunahAi 2048-Calibration FP8
Calibration Samples	128-512	2,048
Datasets	Single generic	4 code-focused
Edge Case Handling	Adequate	Superior
Code Quality	Good	Excellent
Production Ready	Maybe	Absolutely