README.md

---
license: apache-2.0
base_model: ibm-granite/granite-20b-code-instruct-8k
tags:
  - fp8
  - quantized
  - code
  - granite
  - ibm
  - llmcompressor
  - vllm
library_name: transformers
pipeline_tag: text-generation
---

# granite-20b-code-instruct-8k-FP8

**FP8 quantized version of IBM's Granite 20B Code model for efficient inference**

This is an FP8 (E4M3) quantized version of [ibm-granite/granite-20b-code-instruct-8k](https://huggingface.co/ibm-granite/granite-20b-code-instruct-8k) using compressed_tensors format. Quantized by [TevunahAi](https://huggingface.co/TevunahAi) on enterprise-grade hardware.

## 🎯 Recommended Usage: vLLM

For optimal performance with **full FP8 benefits** (2x memory savings + faster inference), use **vLLM** or **TensorRT-LLM**:

### Quick Start with vLLM

```bash
pip install vllm
```

**Python API:**

```python
from vllm import LLM, SamplingParams

# vLLM auto-detects FP8 from model config
llm = LLM(model="TevunahAi/granite-20b-code-instruct-8k-FP8", dtype="auto")

# Generate
prompt = "Write a Python function to calculate fibonacci numbers:"
sampling_params = SamplingParams(temperature=0.7, max_tokens=256)

outputs = llm.generate([prompt], sampling_params)
for output in outputs:
    print(output.outputs[0].text)
```

**OpenAI-Compatible API Server:**

```bash
vllm serve TevunahAi/granite-20b-code-instruct-8k-FP8 \
    --dtype auto \
    --max-model-len 8192
```

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/granite-20b-code-instruct-8k-FP8",
    messages=[
        {"role": "user", "content": "Write a Python function to calculate fibonacci numbers"}
    ],
    temperature=0.7,
    max_tokens=256,
)

print(response.choices[0].message.content)
```

### vLLM Benefits

- ✅ **Weights, activations, and KV cache in FP8**
- ✅ **~20GB 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 (Not Recommended)

This model can be loaded with `transformers`, but **will decompress FP8 → BF16 during inference**, requiring ~40GB+ VRAM. For 20B models, **vLLM is strongly recommended**.

<details>
<summary>Transformers Example (Click to expand)</summary>

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Loads FP8 weights but decompresses to BF16 during compute
model = AutoModelForCausalLM.from_pretrained(
    "TevunahAi/granite-20b-code-instruct-8k-FP8",
    device_map="auto",
    torch_dtype="auto",
    low_cpu_mem_usage=True,
)
tokenizer = AutoTokenizer.from_pretrained("TevunahAi/granite-20b-code-instruct-8k-FP8")

# Generate
prompt = "Write a Python function to calculate fibonacci numbers:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
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:**
- **~40GB+ VRAM** (decompressed to BF16)
- Multi-GPU setup or A100/H100
- CUDA 11.8 or newer

**⚠️ Warning:** vLLM is the recommended deployment method for 20B models.

</details>

## 📊 Quantization Details

| Property | Value |
|----------|-------|
| **Base Model** | [ibm-granite/granite-20b-code-instruct-8k](https://huggingface.co/ibm-granite/granite-20b-code-instruct-8k) |
| **Quantization Method** | FP8 E4M3 weight-only |
| **Framework** | llm-compressor + compressed_tensors |
| **Calibration Dataset** | open_platypus (512 samples) |
| **Storage Size** | ~20GB (sharded safetensors) |
| **VRAM (vLLM)** | ~20GB |
| **VRAM (Transformers)** | ~40GB+ (decompressed to BF16) |
| **Target Hardware** | NVIDIA Ada (RTX 4000/5000) or Hopper (H100/GH200) |
| **Quantization Time** | 46.5 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 20B Models?

### With vLLM/TensorRT-LLM:
- ✅ **50% memory reduction** vs BF16 (weights + activations + KV cache)
- ✅ **Single GPU deployment** on RTX 4090 (24GB) or RTX 5000 Ada (32GB)
- ✅ **Faster inference** via native FP8 tensor cores
- ✅ **Better throughput** with optimized kernels
- ✅ **Minimal quality loss** for code generation tasks

### With Transformers:
- ✅ **Smaller download size** (~20GB vs ~40GB BF16)
- ✅ **Compatible** with standard transformers workflow
- ⚠️ **Decompresses to BF16** during inference (no runtime memory benefit)
- ❌ **Requires 40GB+ VRAM** - impractical for most setups

**For 20B models, vLLM is essential for practical deployment.**

## 💾 Model Files

This model is sharded into multiple safetensors files (all required for inference). The compressed format enables efficient storage and faster downloads.

## 🔬 IBM Granite Code Models

Granite Code models are specifically trained for code generation, editing, and explanation tasks. This 20B parameter version offers strong performance on:

- Code completion and generation
- Bug fixing and refactoring
- Code explanation and documentation
- Multiple programming languages
- 8K context window

## 📚 Original Model

This quantization is based on [ibm-granite/granite-20b-code-instruct-8k](https://huggingface.co/ibm-granite/granite-20b-code-instruct-8k) by IBM.

For comprehensive information about:
- Model architecture and training methodology
- Supported programming languages
- Evaluation benchmarks and results
- Ethical considerations and responsible AI guidelines

Please refer to the [original model card](https://huggingface.co/ibm-granite/granite-20b-code-instruct-8k).

## 🔧 Hardware Requirements

### Minimum (vLLM):
- **GPU:** NVIDIA RTX 4090 (24GB) or RTX 5000 Ada (32GB)
- **VRAM:** 20GB minimum, 24GB+ recommended
- **CUDA:** 11.8 or newer

### Recommended (vLLM):
- **GPU:** NVIDIA RTX 5000 Ada (32GB) / H100 (80GB)
- **VRAM:** 24GB+
- **CUDA:** 12.0+

### Transformers:
- **GPU:** Multi-GPU setup or A100 (40GB+)
- **VRAM:** 40GB+ (single GPU) or distributed across multiple GPUs
- **Not recommended** for practical deployment

## 📖 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)
- **IBM Granite:** [github.com/ibm-granite](https://github.com/ibm-granite)

## 📄 License

This model inherits the **Apache 2.0 License** from the original Granite model.

## 🙏 Acknowledgments

- **Original Model:** IBM Granite 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 Granite work:

```bibtex
@misc{granite2024,
  title={Granite Code Models},
  author={IBM Research},
  year={2024},
  url={https://huggingface.co/ibm-granite/granite-20b-code-instruct-8k}
}
```

---

<div align="center">

**Professional AI Model Quantization by TevunahAi**

*Enterprise-grade quantization on specialized hardware*

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

</div>