Create README.md

README.md

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+---
+tags:
+- fp8
+- vllm
+---
+
+# Meta-Llama-3-70B-Instruct-FP8-KV
+
+## Model Overview
+Meta-Llama-3-70B-Instruct quantized to FP8 weights and activations using per-tensor quantization, ready for inference with vLLM >= 0.5.0.
+This model checkpoint also includes per-tensor scales for FP8 quantized KV Cache, accessed through the `--kv-cache-dtype fp8` argument in vLLM.
+
+```python
+from vllm import LLM
+model = LLM(model="neuralmagic/Meta-Llama-3-70B-Instruct-FP8-KV", kv_cache_dtype="fp8")
+result = model.generate("Hello, my name is")
+```
+
+## Usage and Creation
+Produced using [AutoFP8 with calibration samples from ultrachat](https://github.com/neuralmagic/AutoFP8).
+
+```python
+from datasets import load_dataset
+from transformers import AutoTokenizer
+
+from auto_fp8 import AutoFP8ForCausalLM, BaseQuantizeConfig
+
+pretrained_model_dir = "meta-llama/Meta-Llama-3-70B-Instruct"
+quantized_model_dir = "Meta-Llama-3-70B-Instruct-FP8-KV"
+
+tokenizer = AutoTokenizer.from_pretrained(pretrained_model_dir, use_fast=True)
+tokenizer.pad_token = tokenizer.eos_token
+
+ds = load_dataset("mgoin/ultrachat_2k", split="train_sft")
+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",
+    ignore_patterns=["re:.*lm_head"],
+    kv_cache_quant_targets=("k_proj", "v_proj"),
+)
+
+model = AutoFP8ForCausalLM.from_pretrained(pretrained_model_dir, quantize_config)
+model.quantize(examples)
+model.save_quantized(quantized_model_dir)
+```
+
+## Evaluation
+
+### Open LLM Leaderboard evaluation scores
+|                          | Meta-Llama-3-70B-Instruct | Meta-Llama-3-70B-Instruct-FP8 | Meta-Llama-3-70B-Instruct-FP8-KV<br>(this model) |
+| :----------------------: | :-----------------------: | :---------------------------: | :----------------------------------------------: | 
+| arc-c<br>25-shot         | 72.69                     | 72.61                         | 72.57                                            |
+| hellaswag<br>10-shot     | 85.50                     | 85.41                         | 85.32                                            |
+| mmlu<br>5-shot           | 80.18                     | 80.06                         | 79.69                                            |
+| truthfulqa<br>0-shot     | 62.90                     | 62.73                         | 61.92                                            |
+| winogrande<br>5-shot     | 83.34                     | 83.03                         | 83.66                                            |
+| gsm8k<br>5-shot          | 92.49                     | 91.12                         | 90.83                                            |
+| **Average<br>Accuracy**  | **79.51**                 | **79.16**                     | **79.00**                                        |
+| **Recovery**             | **100%**                  | **99.55%**                    | **99.36%**                                       |