README.md

---
license: mit
base_model: microsoft/NextCoder-32B
tags:
- code
- fp8
- quantized
- nextcoder
- microsoft
library_name: transformers
pipeline_tag: text-generation
---

# NextCoder-32B-FP8

**High-quality FP8 quantization of Microsoft's NextCoder-32B, optimized for production inference**

This is an FP8 (E4M3) quantized version of [microsoft/NextCoder-32B](https://huggingface.co/microsoft/NextCoder-32B) using compressed_tensors format. Quantized by [TevunahAi](https://huggingface.co/TevunahAi) on enterprise-grade hardware with 2048 calibration samples.

## 🎯 Recommended Usage: vLLM (Required)

For 32B models, **vLLM is essential** for practical deployment. FP8 quantization makes this flagship 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-FP8", dtype="auto")

# Prepare prompt with chat template
tokenizer = AutoTokenizer.from_pretrained("TevunahAi/NextCoder-32B-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:**

```bash
vllm serve TevunahAi/NextCoder-32B-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-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, RTX 6000 Ada, A100 80GB)
- ✅ **Native FP8 tensor core acceleration**
- ✅ **Production-grade performance**

## ⚠️ 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-FP8",
    device_map="auto",  # Will distribute across GPUs
    torch_dtype="auto",
    low_cpu_mem_usage=True,
)
tokenizer = AutoTokenizer.from_pretrained("TevunahAi/NextCoder-32B-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:**
```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 A100 80GB / H100 80GB
- Not practical for most deployments

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

</details>

## 📊 Quantization Details

| Property | Value |
|----------|-------|
| **Base Model** | [microsoft/NextCoder-32B](https://huggingface.co/microsoft/NextCoder-32B) |
| **Quantization Method** | FP8 E4M3 weight-only |
| **Framework** | llm-compressor + compressed_tensors |
| **Storage Size** | ~32GB (sharded safetensors) |
| **VRAM (vLLM)** | ~32GB |
| **VRAM (Transformers)** | ~64GB+ (decompressed to BF16) |
| **Target Hardware** | NVIDIA H100, A100 80GB, RTX 6000 Ada |
| **Quantization Date** | November 23, 2025 |
| **Quantization Time** | 213.8 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 for 32B 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 consumer/prosumer GPUs
- ✅ **Minimal quality loss** (sub-1% perplexity increase)

### Without FP8:
- ❌ BF16 requires ~64GB VRAM (H100 80GB 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 sharded into multiple safetensors files (all required for inference). The compressed format enables efficient storage and faster downloads.

## 🚀 Performance Comparison

The 32B model represents the flagship tier:

| Model | VRAM (vLLM) | Quality | Use Case |
|-------|-------------|---------|----------|
| **7B-FP8** | ~7GB | Good | General coding, fast iteration |
| **14B-FP8** | ~14GB | Better | Complex tasks, better reasoning |
| **32B-FP8** | ~32GB | Best | Flagship performance, production |

**32B Benefits:**
- ✅ **State-of-the-art code quality** for Microsoft NextCoder family
- ✅ **Superior reasoning** and complex problem solving
- ✅ **Enterprise-grade completions** for mission-critical applications
- ✅ **Best context understanding** across the model family


## 📚 Original Model

This quantization is based on [microsoft/NextCoder-32B](https://huggingface.co/microsoft/NextCoder-32B) 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](https://huggingface.co/microsoft/NextCoder-32B).

## 🔧 Hardware Requirements

### Minimum (vLLM):
- **GPU:** NVIDIA A100 40GB or RTX 6000 Ada (48GB)
- **VRAM:** 32GB minimum, 40GB+ recommended
- **CUDA:** 11.8 or newer

### Recommended (vLLM):
- **GPU:** NVIDIA H100 (80GB) / A100 80GB / RTX 6000 Ada (48GB)
- **VRAM:** 40GB+
- **CUDA:** 12.0+

### Transformers:
- **GPU:** Multi-GPU setup (2x A100 40GB) or single A100/H100 80GB
- **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}
}
```

---

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