WestLake-7B-laser-truthy-dpo-GGUF

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iMatrix

iMatrix quantizations are now available thanks to user Ji-Ha

Chat Template

*I am using ooba (text generation web ui) for inference

The GGUF version defaults to alpaca:

11:40:53-940260 INFO LOADER: llama.cpp
11:40:53-940970 INFO TRUNCATION LENGTH: 32768
11:40:53-941299 INFO INSTRUCTION TEMPLATE: Alpaca
11:40:53-941580 INFO Loaded the model in 4.55 seconds.

{%- set ns = namespace(found=false) -%}
{%- for message in messages -%}
 {%- if message['role'] == 'system' -%}
 {%- set ns.found = true -%}
 {%- endif -%}
{%- endfor -%}
{%- if not ns.found -%}
 {{- '' + 'Below is an instruction that describes a task. Write a response that appropriately completes the request.' + '\n\n' -}}
{%- endif %}
{%- for message in messages %}
 {%- if message['role'] == 'system' -%}
 {{- '' + message['content'] + '\n\n' -}}
 {%- else -%}
 {%- if message['role'] == 'user' -%}
 {{-'### Instruction:\n' + message['content'] + '\n\n'-}}
 {%- else -%}
 {{-'### Response:\n' + message['content'] + '\n\n' -}}
 {%- endif -%}
 {%- endif -%}
{%- endfor -%}
{%- if add_generation_prompt -%}
 {{-'### Response:\n'-}}
{%- endif -%}

And can be manually overridden to chatML successfully:

{%- set ns = namespace(found=false) -%}
{%- for message in messages -%}
 {%- if message['role'] == 'system' -%}
 {%- set ns.found = true -%}
 {%- endif -%}
{%- endfor -%}
{%- if not ns.found -%}
 {{- '<|im_start|>system\n' + '' + '<|im_end|>\n' -}}
{%- endif %}
{%- for message in messages %}
 {%- if message['role'] == 'system' -%}
 {{- '<|im_start|>system\n' + message['content'] + '<|im_end|>\n' -}}
 {%- else -%}
 {%- if message['role'] == 'user' -%}
 {{-'<|im_start|>user\n' + message['content'] + '<|im_end|>\n'-}}
 {%- else -%}
 {{-'<|im_start|>assistant\n' + message['content'] + '<|im_end|>\n' -}}
 {%- endif -%}
 {%- endif -%}
{%- endfor -%}
{%- if add_generation_prompt -%}
 {{-'<|im_start|>assistant\n'-}}
{%- endif -%}

Here is the output of the model when I override the chat template to chatML

<|im_start|>system <|im_end|> <|im_start|>user write me a quicksort algorithm in python<|im_end|> <|im_start|>assistant Sure, here's a basic implementation of Quicksort algorithm in Python:

def partition(array, low, high):
 """Sorts elements around pivot"""
 i = (low - 1) # Index of smaller element
 pivot = array[high] # Select rightmost element as pivot

 for j in range(low, high):
 if array[j] <= pivot:
 i += 1
 array[i], array[j] = array[j], array[i]

 array[i + 1], array[high] = array[high], array[i + 1] # Put the pivot where it belongs
 return (i + 1) # Return index after pivot point

def quicksort(array, low, high):
 """Implementing Quicksort"""
 if len(array) == 1 or low >= high: # Base conditions
 return array
 elif low < high: # Recursive step
 pi = partition(array, low, high) # Separate smaller and greater elements around pivot point
 quicksort(array, low, pi - 1) # Sort first half
 quicksort(array, pi + 1, high) # Sort second half
 return array # After recursion ends we have a sorted list

To use it:

arr = [45, 23, 76, 89, 32, 65] # Your unsorted array
print("Original Array:", arr) # Print original array
quicksort(arr, 0, len(arr)-1) # Call Quicksort function with given range for sorting the entire array.
print("Sorted Array:", arr) # Print sorted array

<|im_end|> ```

2 items • Updated Mar 2

5 items • Updated Mar 2 • 1