NextCoder-14B-2048-Calibration-FP8

This is a premium FP8 quantized version of microsoft/NextCoder-14B featuring rigorous code-optimized multi-dataset calibration for production-grade reliability.

Model Description

Property	Value
Base Model	NextCoder-14B
Architecture	Dense (14B parameters)
Quantization	FP8 (E4M3 format) via llm-compressor
Target Hardware	NVIDIA Ada Lovelace & Hopper GPUs
Quantization Date	2025-11-27
Quantization Time	91.3 minutes
Calibration Samples	2,048 (premium code-optimized)

Usage

With Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "TevunahAi/NextCoder-14B-2048-Calibration-FP8",
    torch_dtype=torch.float8_e4m3fn,
    device_map="auto",
    low_cpu_mem_usage=True,
)

tokenizer = AutoTokenizer.from_pretrained("TevunahAi/NextCoder-14B-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-14B-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: ~14GB (fits on RTX 4090, RTX 4080, or A100)

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: 91.3 minutes with premium 2048-sample calibration
Memory during quantization: ~170GB (model + calibration datasets)
Memory reduction: ~~28GB FP16 → ~14GB FP8 (~~50% reduction)
Inference speed: 2-3x faster on Ada Lovelace GPUs vs FP16

About NextCoder

NextCoder-14B is Microsoft's next-generation code model, featuring:

State-of-the-art code generation capabilities
Strong performance across multiple programming languages
Excellent instruction following for coding tasks
Larger capacity than 7B for complex coding tasks
MIT license

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