NextCoder-32B-2048-Calibration-FP8

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

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

🎯 Recommended Usage: vLLM (Required)

For 32B models, vLLM is essential for practical deployment. Premium FP8 quantization makes this flagship code model accessible on high-end consumer GPUs.

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-32B-2048-Calibration-FP8", dtype="auto")

# Prepare prompt with chat template
tokenizer = AutoTokenizer.from_pretrained("TevunahAi/NextCoder-32B-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-32B-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-32B-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
✅ ~32GB VRAM (50% reduction vs BF16's ~64GB)
✅ Single high-end GPU deployment (H100, A100 80GB, RTX 6000 Ada)
✅ Native FP8 tensor core acceleration
✅ Premium 2048-sample code-optimized calibration
✅ Flagship code generation quality

⚠️ Transformers: Not Practical

At 32B parameters, transformers will decompress to ~64GB+ VRAM, requiring multi-GPU setups or data center GPUs. This is not recommended for deployment.

Transformers Example (Multi-GPU Required - Click to expand)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Requires multi-GPU or 80GB+ single GPU
model = AutoModelForCausalLM.from_pretrained(
    "TevunahAi/NextCoder-32B-2048-Calibration-FP8",
    device_map="auto",  # Will distribute across GPUs
    torch_dtype="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))

Requirements:

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

System Requirements:

~64GB+ VRAM (decompressed to BF16)
Multi-GPU setup or H100 NVL
Not practical for most deployments

⚠️ Critical: Use vLLM instead. Transformers is only viable for research/testing with multi-GPU setups.

📊 Model Details

Property	Value
Base Model	microsoft/NextCoder-32B
Architecture	Dense (32B 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	~32GB
VRAM (vLLM)	~32GB
VRAM (Transformers)	~64GB+ (decompressed to BF16)
Target Hardware	NVIDIA H100, A100 80GB, RTX 6000 Ada
Quantization Date	November 27, 2025
Quantization Time	194.0 minutes (~3.2 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 32B Code Models?

With vLLM/TensorRT-LLM:

✅ Enables single-GPU deployment (~32GB vs ~64GB BF16)
✅ 50% memory reduction across weights, activations, and KV cache
✅ Faster inference via native FP8 tensor cores
✅ Makes flagship model accessible on high-end prosumer GPUs
✅ Premium calibration maintains code quality

Without FP8:

❌ BF16 requires ~64GB VRAM (H100 NVL or multi-GPU)
❌ Limited deployment options
❌ Higher infrastructure costs

FP8 quantization transforms 32B from "data center only" to "high-end workstation deployable".

💾 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 represents state-of-the-art code generation. The 32B version is the flagship tier:

Model	Parameters	VRAM (vLLM)	Quant Time	Quality	Use Case
7B	7B	~7GB	51 min	Good	Fast iteration, prototyping
14B	14B	~14GB	91 min	Better	Complex tasks, better reasoning
32B	32B	~32GB	194 min	Best	Flagship performance, production

32B Benefits:

✅ State-of-the-art code quality for NextCoder family
✅ Superior reasoning for complex algorithms
✅ Best context understanding for large codebases
✅ Enterprise-grade completions for mission-critical applications
✅ MIT license for commercial use

📈 TevunahAi NextCoder Premium Quantizations

All premium quantizations use identical 2048-sample code-focused calibration:

Model	Parameters	Calibration	Samples	Quant Time	VRAM
NextCoder-7B-2048-FP8	7B	Code-optimized	2,048	51 min	~7GB
NextCoder-14B-2048-FP8	14B	Code-optimized	2,048	91 min	~14GB
NextCoder-32B-2048-FP8 (this)	32B	Code-optimized	2,048	194 min	~32GB

⚖️ 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	~80 min	Quick deployment
Premium FP8 (this)	Code-optimized	2,048	4 code-focused	194 min	Production-grade

When to Choose Premium:

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

When Standard is Fine:

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

🔬 Quantization Infrastructure

Professional hardware pushing the limits:

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 (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:

3.2 hours of rigorous quantization and validation
319GB RAM required - impossible on consumer hardware
Code-specific calibration requires specialized datasets
Professional infrastructure enables quality impossible on standard setups

📚 Original Model

This quantization is based on microsoft/NextCoder-32B by Microsoft.

NextCoder-32B is the flagship 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 for commercial use

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

🔧 Hardware Requirements

Minimum (vLLM):

GPU: NVIDIA A100 80GB or RTX 6000 Ada (48GB)
VRAM: 32GB minimum, 40GB+ recommended
CUDA: 11.8 or newer

Recommended (vLLM):

GPU: NVIDIA H100 (80GB) / H100 NVL / RTX 6000 Ada (48GB)
VRAM: 40GB+
CUDA: 12.0+

Transformers:

GPU: Multi-GPU setup (2x A100 40GB) or H100 NVL
VRAM: 64GB+ total
Not recommended - use vLLM instead

📖 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-32B 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-32B}
}

🌟 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	~80 min	194 min (3.2 hours)
Peak RAM Usage	~150GB	319GB
Edge Case Handling	Adequate	Superior
Code Quality	Good	Excellent
Production Ready	Maybe	Absolutely
Infrastructure	Consumer/Prosumer	Enterprise-grade