TevunahAi/NextCoder-32B-2048-Calibration-FP8

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.

<details> <summary>Transformers Example (Multi-GPU Required - Click to expand)</summary>

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.

</details>

📊 Model Details

🏆 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

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:

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:

⚖️ Comparison: Standard vs Premium Calibration

TevunahAi offers two quantization tiers for this model:

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:

The right calibration for the right model.

The Difference is in the Details

Professional Infrastructure

• —2.6 TB/s aggregate memory bandwidth
• —319GB peak RAM during 32B quantization
• —2,048 samples across 4 code-focused datasets
• —Quality-first approach over speed
• —Enterprise-ready results for production code generation

When deploying flagship 32B code models in production, accept no compromises.

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