Update README.md

README.md

@@ -8,35 +8,4 @@ base_model: Sao10K/L3-8B-Stheno-v3.2
 
 Original Model: https://huggingface.co/Sao10K/L3-8B-Stheno-v3.2
 
-Quantized with AutoGPTQ 128g wikitext2, using the script from https://aphrodite.pygmalion.chat/pages/quantization/quantization-methods.html#gptq
-
-Script:
-```python
-from datasets import load_dataset
-from transformers import AutoTokenizer
-
-from auto_fp8 import AutoFP8ForCausalLM, BaseQuantizeConfig
-
-pretrained_model_dir = "Sao10K/L3-8B-Stheno-v3.2"
-quantized_model_dir = "L3-8B-Stheno-v3.2-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",
-    ignore_patterns=["re:.*lm_head"],
-)
-
-model = AutoFP8ForCausalLM.from_pretrained(
-    pretrained_model_dir, quantize_config=quantize_config
-)
-
-model.quantize(examples)
-model.save_quantized(quantized_model_dir)
-```
+Quantized with AutoGPTQ 128g wikitext2, using the script from https://aphrodite.pygmalion.chat/pages/quantization/quantization-methods.html#gptq