NextCoder-14B-2048-Calibration-FP8

Premium FP8 quantization with 2,048 code-optimized calibration samples

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

🎯 Recommended Usage: vLLM

For optimal performance with full FP8 benefits and code-optimized quality, use vLLM or TensorRT-LLM:

Quick Start with vLLM

pip install vllm

Python API:

from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

# vLLM auto-detects FP8 from model config
llm = LLM(model="TevunahAi/NextCoder-14B-2048-Calibration-FP8", dtype="auto")

# Prepare prompt with chat template
tokenizer = AutoTokenizer.from_pretrained("TevunahAi/NextCoder-14B-2048-Calibration-FP8")
messages = [{"role": "user", "content": "Write a Python function to calculate fibonacci numbers"}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

# Generate
sampling_params = SamplingParams(temperature=0.7, max_tokens=512)
outputs = llm.generate([prompt], sampling_params)

for output in outputs:
    print(output.outputs[0].text)

OpenAI-Compatible API Server:

vllm serve TevunahAi/NextCoder-14B-2048-Calibration-FP8 \
    --dtype auto \
    --max-model-len 4096

Then use with OpenAI client:

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="token-abc123",  # dummy key
)

response = client.chat.completions.create(
    model="TevunahAi/NextCoder-14B-2048-Calibration-FP8",
    messages=[
        {"role": "user", "content": "Write a Python function to calculate fibonacci numbers"}
    ],
    temperature=0.7,
    max_tokens=512,
)

print(response.choices[0].message.content)

vLLM Benefits

✅ Weights, activations, and KV cache in FP8
✅ ~14GB VRAM (50% reduction vs BF16)
✅ Native FP8 tensor core acceleration on Ada/Hopper GPUs
✅ Single GPU deployment on RTX 4090, RTX 5000 Ada, or H100
✅ Premium 2048-sample code-optimized calibration
✅ Production-grade code quality

⚙️ Alternative: Transformers (Not Recommended)

This model can be loaded with transformers, but will decompress FP8 → BF16 during inference, requiring ~28GB+ VRAM. For 14B models, vLLM is strongly recommended.

Transformers Example (Click to expand)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Loads FP8 weights but decompresses to BF16 during compute
model = AutoModelForCausalLM.from_pretrained(
    "TevunahAi/NextCoder-14B-2048-Calibration-FP8",
    device_map="auto",
    torch_dtype="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))

Requirements:

pip install torch>=2.1.0 transformers>=4.40.0 accelerate compressed-tensors

System Requirements:

~28GB+ VRAM (decompressed to BF16)
Multi-GPU setup or high-end single GPU
CUDA 11.8 or newer

⚠️ Warning: Most consumer GPUs will struggle with transformers inference at this size. Use vLLM for practical deployment.

📊 Model Details

Property	Value
Base Model	microsoft/NextCoder-14B
Architecture	Dense (14B parameters)
Quantization Method	FP8 E4M3 weight-only
Framework	llm-compressor + compressed_tensors
Calibration Samples	2,048 (4-8x industry standard)
Calibration Type	Code-optimized (4 datasets)
Storage Size	~14GB
VRAM (vLLM)	~14GB
VRAM (Transformers)	~28GB+ (decompressed to BF16)
Target Hardware	NVIDIA RTX 4090, RTX 5000 Ada, H100
Quantization Date	November 27, 2025
Quantization Time	91.3 minutes (~1.5 hours)

🏆 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.

🔧 Why FP8 for Code Models?

With vLLM/TensorRT-LLM:

✅ 50% memory reduction vs BF16 (weights + activations + KV cache)
✅ Single GPU deployment on RTX 4090 (24GB) or RTX 5000 Ada (32GB)
✅ Faster inference via native FP8 tensor cores
✅ Better throughput with optimized kernels
✅ Code-optimized calibration maintains quality

With Transformers:

✅ Smaller download size (~14GB vs ~28GB BF16)
✅ Compatible with standard transformers workflow
⚠️ Decompresses to BF16 during inference (no runtime memory benefit)
❌ Requires 28GB+ VRAM - impractical for most setups

For 14B code models, vLLM is essential for practical deployment.

💾 Model Files

This model is stored as sharded safetensors files (all required for inference). The compressed format enables efficient storage and faster downloads.

🚀 NextCoder Model Family

Microsoft's NextCoder family offers state-of-the-art code generation. The 14B version provides the sweet spot:

Model	Parameters	VRAM (vLLM)	Quality	Use Case
7B	7B	~7GB	Good	Fast iteration, prototyping
14B	14B	~14GB	Better	Complex tasks, better reasoning
32B	32B	~32GB	Best	Flagship performance, production

14B Benefits:

✅ 2x capacity vs 7B model
✅ Superior reasoning for complex algorithms
✅ Better context handling for larger codebases
✅ Single GPU deployment on RTX 4090/5000 Ada
✅ Excellent quality-per-cost ratio

⚖️ Comparison: Standard vs Premium Calibration

TevunahAi offers two quantization tiers for this model:

Version	Calibration	Samples	Datasets	Quant Time	Use Case
Standard FP8	Basic	512	1 generic	~35 min	Quick deployment
Premium FP8 (this)	Code-optimized	2,048	4 code-focused	91 min	Production-grade

When to Choose Premium:

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

When Standard is Fine:

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

🔬 Quantization Infrastructure

Professional hardware for premium calibration:

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: ~170GB during quantization (model + calibration datasets)
GPU: NVIDIA RTX 5000 Ada Generation (32GB VRAM, native FP8 support)
Software: Ubuntu 25.10 | Python 3.12 | PyTorch 2.8 | CUDA 13.0 | llm-compressor

Why This Matters:

91 minutes of rigorous quantization and validation
Code-specific calibration requires specialized datasets
Professional infrastructure enables quality impossible on consumer setups

📚 Original Model

This quantization is based on microsoft/NextCoder-14B by Microsoft.

NextCoder-14B features:

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 for commercial use

For comprehensive information, please refer to the original model card.

🔧 Hardware Requirements

Minimum (vLLM):

GPU: NVIDIA RTX 4090 (24GB) or RTX 5000 Ada (32GB)
VRAM: 14GB minimum, 16GB+ recommended
CUDA: 11.8 or newer

Recommended (vLLM):

GPU: NVIDIA RTX 4090 / RTX 5000 Ada / H100
VRAM: 24GB+
CUDA: 12.0+

Transformers:

GPU: Multi-GPU setup or high-end single GPU (32GB+)
VRAM: 28GB+ (single GPU) or distributed
Not recommended for practical deployment

📖 Additional Resources

vLLM Documentation: docs.vllm.ai
TensorRT-LLM: github.com/NVIDIA/TensorRT-LLM
TevunahAi Models: huggingface.co/TevunahAi
llm-compressor: github.com/vllm-project/llm-compressor

📄 License

This model inherits the MIT License from the original NextCoder-14B model.

🙏 Acknowledgments

Original Model: Microsoft NextCoder team
Quantization Framework: Neural Magic's llm-compressor
Quantized by: TevunahAi

📝 Citation

If you use this model, please cite the original NextCoder work:

@misc{nextcoder2024,
  title={NextCoder: Next-Generation Code LLM},
  author={Microsoft},
  year={2024},
  url={https://huggingface.co/microsoft/NextCoder-14B}
}

🌟 Why TevunahAi Premium Calibration FP8?

Task-Optimized Calibration

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

Model Type	Calibration Focus	Example Datasets
Code Models	Code-specific	CodeAlpaca, evol-codealpaca
General Models	Diverse instructions	UltraChat, SlimOrca
MoE Models	Balanced distribution	Multi-task datasets

The right calibration for the right model.

The Difference is in the Details

Aspect	Standard FP8	TevunahAi Premium FP8
Calibration Samples	128-512	2,048
Datasets	Single generic	4 code-focused
Calibration Time	~35 min	91 min
Edge Case Handling	Adequate	Superior
Code Quality	Good	Excellent
Production Ready	Maybe	Absolutely
Infrastructure	Consumer/Prosumer	Enterprise-grade