NextCoder-7B-FP8

High-quality FP8 quantization of Microsoft's NextCoder-7B, optimized for production inference

This is an FP8 (E4M3) quantized version of microsoft/NextCoder-7B using compressed_tensors format. Quantized by TevunahAi on enterprise-grade hardware with 2048 calibration samples.

🎯 Recommended Usage: vLLM

For optimal performance with full FP8 benefits (2x memory savings + faster inference), 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-7B-FP8", dtype="auto")

# Prepare prompt with chat template
tokenizer = AutoTokenizer.from_pretrained("TevunahAi/NextCoder-7B-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
outputs = llm.generate(prompt, SamplingParams(temperature=0.7, max_tokens=512))
print(outputs[0].outputs[0].text)

OpenAI-Compatible API Server:

vllm serve TevunahAi/NextCoder-7B-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-7B-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
• ✅ ~7GB VRAM (50% reduction vs BF16)
• ✅ Native FP8 tensor core acceleration on Ada/Hopper GPUs
• ✅ Faster inference with optimized CUDA kernels
• ✅ Production-grade performance

⚙️ Alternative: Transformers

This model can also be loaded with transformers. Note: Transformers will decompress FP8 → BF16 during inference, losing the memory benefit. However, at 7B parameters, this is manageable (~14GB VRAM).

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-7B-FP8",
    device_map="auto",
    torch_dtype="auto",
    low_cpu_mem_usage=True,
)
tokenizer = AutoTokenizer.from_pretrained("TevunahAi/NextCoder-7B-FP8")

# Generate code
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,
    temperature=0.7,
    do_sample=True
)
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:

• ~14GB VRAM (decompressed to BF16)
• CUDA 11.8 or newer
• PyTorch 2.1+ with CUDA support

📊 Quantization Details

Property	Value
Base Model	microsoft/NextCoder-7B
Quantization Method	FP8 E4M3 weight-only
Framework	llm-compressor + compressed_tensors
Storage Size	~7GB (3 sharded safetensors)
VRAM (vLLM)	~7GB
VRAM (Transformers)	~14GB (decompressed to BF16)
Target Hardware	NVIDIA Ada (RTX 4000/5000) or Hopper (H100/GH200)
Quantization Date	November 22, 2025
Quantization Time	47 minutes

Quantization Infrastructure

Professional hardware ensures consistent, high-quality quantization:

• CPUs: Dual Intel Xeon Max 9480 (112 cores / 224 threads, 128GB HBM2e)
• GPU: NVIDIA RTX 5000 Ada Generation (32GB VRAM, native FP8 support)
• Memory: 256GB DDR5 + 128GB HBM2e = 384GB total system memory
• Software Stack: Ubuntu 25.10 | Python 3.12 | PyTorch 2.8 | CUDA 13.0 | llm-compressor

🔧 Why FP8?

With vLLM/TensorRT-LLM:

• ✅ 50% memory reduction vs BF16 (weights + activations + KV cache)
• ✅ Faster inference via native FP8 tensor cores
• ✅ Minimal quality loss (sub-1% perplexity increase)
• ✅ Better throughput with optimized kernels

With Transformers:

• ✅ Smaller download size (~7GB vs ~14GB BF16)
• ✅ Compatible with standard transformers workflow
• ⚠️ Decompresses to BF16 during inference (no runtime memory benefit)

For production inference, use vLLM to realize the full FP8 benefits.

💾 Model Files

This model is sharded into 3 safetensors files (all required for inference):

• model-00001-of-00003.safetensors
• model-00002-of-00003.safetensors
• model-00003-of-00003.safetensors

📚 Original Model

This quantization is based on microsoft/NextCoder-7B by Microsoft.

For comprehensive information about:

• Model architecture and training methodology
• Capabilities, use cases, and limitations
• Evaluation benchmarks and results
• Ethical considerations and responsible AI guidelines

Please refer to the original model card.

🔧 Hardware Requirements

Minimum (vLLM):

• GPU: NVIDIA RTX 4060 Ti (16GB) or better
• VRAM: 8GB minimum, 16GB recommended
• CUDA: 11.8 or newer

Recommended (vLLM):

• GPU: NVIDIA RTX 4090 / RTX 5000 Ada / H100
• VRAM: 16GB+
• CUDA: 12.0+

Transformers:

• GPU: Any CUDA-capable GPU
• VRAM: 16GB+ (due to BF16 decompression)

📖 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-7B 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-7B}
}

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