Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+base_model: microsoft/NextCoder-32B
+tags:
+- code
+- fp8
+- quantized
+- nextcoder
+- microsoft
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# NextCoder-32B-FP8
+
+This is an FP8 quantized version of [microsoft/NextCoder-32B](https://huggingface.co/microsoft/NextCoder-32B) for efficient inference on NVIDIA Ada Lovelace and newer GPUs.
+
+## Model Description
+
+FP8 (8-bit floating point) quantization of NextCoder-32B, optimized for fast code generation with minimal quality loss.
+
+### Quantization Details
+
+| Property | Value |
+|----------|-------|
+| Original Model | [microsoft/NextCoder-32B](https://huggingface.co/microsoft/NextCoder-32B) |
+| Quantization Method | FP8 (E4M3) via llm-compressor |
+| Model Size | ~64GB (sharded safetensors files) |
+| Target Hardware | NVIDIA Ada Lovelace (RTX 40xx, RTX 5000 Ada, etc.) |
+| Quantization Date | 2025-11-23 |
+| Quantization Time | 213.8 minutes |
+| Hardware Used | NVIDIA RTX 5000 Ada Generation (31.5 GB) |
+
+## Usage
+
+### Loading the Model
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load model with FP8 quantization
+model = AutoModelForCausalLM.from_pretrained(
+    "TevunahAi/NextCoder-32B-FP8",
+    torch_dtype=torch.float8_e4m3fn,  # FP8 dtype
+    device_map="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  # FP8 support requires PyTorch 2.1+
+pip install transformers>=4.40.0
+pip install accelerate
+```
+
+**System Requirements:**
+- PyTorch 2.1 or newer with CUDA support
+- NVIDIA GPU with FP8 support (Ada Lovelace or newer: RTX 40xx series, RTX 5000 Ada, H100, etc.)
+- CUDA 11.8 or newer
+- ~64GB VRAM for inference (or use multi-GPU setup with device_map="auto")
+
+## Benefits of FP8
+
+- **~50% memory reduction** compared to FP16/BF16
+- **Faster inference** on Ada Lovelace and Hopper GPUs with native FP8 Tensor Cores
+- **Minimal quality loss** compared to INT8 or INT4 quantization
+- **Native hardware acceleration** on modern NVIDIA GPUs
+
+## Model Files
+
+This model is sharded into multiple safetensors files. All files are required for inference.
+
+## Original Model
+
+This quantization is based on [microsoft/NextCoder-32B](https://huggingface.co/microsoft/NextCoder-32B) by Microsoft. Please refer to the [original model card](https://huggingface.co/microsoft/NextCoder-32B) for:
+- Training details
+- Intended use cases
+- Capabilities and limitations
+- Evaluation results
+- Ethical considerations
+
+## Quantization Recipe
+
+This model was quantized using llm-compressor with the FP8 E4M3 format. The quantization recipe is included in `recipe.yaml`.
+
+## License
+
+This model inherits the MIT license from the original NextCoder-32B model.
+
+## 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}
+}
+```
+
+## Acknowledgments
+
+- Original model by Microsoft
+- Quantization performed using Neural Magic's llm-compressor
+- Quantized by TevunahAi