Update README.md

README.md

@@ -8,34 +8,12 @@ This model is part of a series of HQQ tests. I make no claims on the performance
 This is a very extreme example of quantization.
 
 ```python
-# Code to inference Hermes with HF Transformers
-# Requires pytorch, transformers, bitsandbytes, sentencepiece, protobuf, and flash-attn packages
-
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from transformers import LlamaTokenizer, MixtralForCausalLM
 from hqq.engine.hf import HQQModelForCausalLM, AutoTokenizer
-import bitsandbytes, flash_attn
 
 tokenizer = AutoTokenizer.from_pretrained('macadeliccc/Nous-Hermes-2-Mixtral-8x7B-DPO-HQQ', trust_remote_code=True)
-model = HQQModelForCasualLM.from_pretrained(
+model = HQQModelForCausalLM.from_pretrained(
     "macadeliccc/Nous-Hermes-2-Mixtral-8x7B-DPO-HQQ",
     torch_dtype=torch.float16,
     device_map="auto"
 )
-
-prompts = [
-    """<|im_start|>system
-You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|>
-<|im_start|>user
-Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.<|im_end|>
-<|im_start|>assistant""",
-    ]
-
-for chat in prompts:
-    print(chat)
-    input_ids = tokenizer(chat, return_tensors="pt").input_ids.to("cuda")
-    generated_ids = model.generate(input_ids, max_new_tokens=750, temperature=0.8, repetition_penalty=1.1, do_sample=True, eos_token_id=tokenizer.eos_token_id)
-    response = tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_space=True)
-    print(f"Response: {response}")
 ```