Create README.md

README.md

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+---
+tags:
+- fp8
+- vllm
+---
+
+See original model card for information about how it was made. This is to enable fast inference use with Hopper level hardware in FP8. I quantized it to FP8 using neuralmagic code below on 4x L40s.
+
+https://huggingface.co/alpindale/magnum-72b-v1
+
+# Magnum-72b-v1-FP8
+
+## Model Overview
+* <h3 style="display: inline;">Model Architecture:</h3> Based on and identical to the Qwen2-72B-Instruct architecture
+* <h3 style="display: inline;">Model Optimizations:</h3> Weights and activations quantized to FP8
+* <h3 style="display: inline;">Release Date:</h3> June 25, 2024
+
+Magnum-72B-v1 quantized to FP8 weights and activations using per-tensor quantization through the [AutoFP8 repository](https://github.com/neuralmagic/AutoFP8), ready for inference with vLLM >= 0.5.0. 
+Calibrated with 512 UltraChat samples to achieve 100% performance recovery on the Open LLM Benchmark evaluations. 
+Reduces space on disk by ~45%.
+Part of the [FP8 LLMs for vLLM collection](https://huggingface.co/collections/neuralmagic/fp8-llms-for-vllm-666742ed2b78b7ac8df13127).
+
+## Usage and Creation
+Produced using [AutoFP8 with calibration samples from ultrachat](https://github.com/neuralmagic/AutoFP8/blob/147fa4d9e1a90ef8a93f96fc7d9c33056ddc017a/example_dataset.py).
+
+```python
+from datasets import load_dataset
+from transformers import AutoTokenizer
+
+from auto_fp8 import AutoFP8ForCausalLM, BaseQuantizeConfig
+
+pretrained_model_dir = "alpindale/magnum-72b-v1"
+quantized_model_dir = "Magnum-72B-FP8"
+
+tokenizer = AutoTokenizer.from_pretrained(pretrained_model_dir, use_fast=True, model_max_length=4096)
+tokenizer.pad_token = tokenizer.eos_token
+
+ds = load_dataset("mgoin/ultrachat_2k", split="train_sft").select(range(512))
+examples = [tokenizer.apply_chat_template(batch["messages"], tokenize=False) for batch in ds]
+examples = tokenizer(examples, padding=True, truncation=True, return_tensors="pt").to("cuda")
+
+quantize_config = BaseQuantizeConfig(quant_method="fp8", activation_scheme="static")
+
+model = AutoFP8ForCausalLM.from_pretrained(
+    pretrained_model_dir, quantize_config=quantize_config
+)
+model.quantize(examples)
+model.save_quantized(quantized_model_dir)
+```