Update README.md

README.md

@@ -38,7 +38,6 @@ These files were quantised using hardware kindly provided by [Latitude.sh](https
 
 ## Prompt template: custom
 
-```
 Please install OpenAI `tiktoken` for the tokenizer.
 
 ```bash
@@ -59,8 +58,6 @@ generated_ids = model.generate(input_ids, max_length=128)
 print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
 ```
 
-```
-
 ## Provided files
 
 Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
@@ -110,6 +107,10 @@ First make sure you have [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) instal
 
 `GITHUB_ACTIONS=true pip install auto-gptq`
 
+```bash
+pip install tiktoken==0.4.0
+```
+
 Then try the following example code:
 
 ```python
@@ -143,28 +144,8 @@ model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
         quantize_config=None)
 """
 
-prompt = "Tell me about AI"
-prompt_template=f'''Please install OpenAI `tiktoken` for the tokenizer.
-
-```bash
-pip install tiktoken==0.4.0
-```
-
-### Causal sampling (code autocompletion)
-
-For regular causal sampling, simply generate completions given the context:
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen25-7b-mono", trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen25-7b-mono")
-
-text = "def hello_world():"
-input_ids = tokenizer(text, return_tensors="pt").input_ids
-generated_ids = model.generate(input_ids, max_length=128)
-print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
-```
-
-'''
+prompt = "def hello_world()"
+prompt_template=f'''{prompt}'''
 
 print("\n\n*** Generate:")